Hint:
Let's say you have a linear transformation $T:\mathbb{R}^n \rightarrow \mathbb{R}^n$.
For convenience, denote $\mathbf{x}_i = T(\mathbf{e}_i)$, where $\mathbf{e}_i$ is the $i$th standard basis vector. So, for example, $\mathbf{e}_1 = \langle 1, 0, 0 \rangle$ in $\mathbb{R}^3$. Likewise, $\mathbf{e}_2 = \langle 0, 1, 0 \rangle$, etc.
Then $T$ is encoded by the $n \times n$ matrix $[\mathbf{x}_1, \mathbf{x}_2, \cdots, \mathbf{x}_n]$.
As a simple example, consider a $90^\circ$ counterclockwise rotation about the origin in $\mathbb{R}^2$. Note that $\langle 1, 0 \rangle \mapsto \langle 0, 1 \rangle$, and further $\langle 0, 1 \rangle \mapsto \langle -1, 0 \rangle$. So our linear transformation is encoded by the matrix $\left[ \begin{array}{ccc}
0 & -1 \\
1 & 0
\end{array} \right]$.
Now applying this to solve your problem will be a bit of work, but if I'm not mistaken, the standard basis vectors lie on the midpoints of edges of this tetrahedron, and edges will map to edges. It'll be your task to figure out precisely where they are mapping to.
Another approach:
Let $\mathbf{v}_1, \mathbf{v}_2, \mathbf{v}_3$, and $\mathbf{v}_4$ denote the vertices of your tetrahedron. Let $T$ be one of the given linear transformations. Then $T(\mathbf{v}_1) = \mathbf{v}_i$ for some $i$, and so forth. In this manner you can arrive at a system of equations whose solution yields the entries of the matrix encoding $T$.