One Hidden Layer, a Batch of Two

A hidden layer of a neural network processes a batch of two examples, each with three features. Rows are examples, columns are features:

$X = \begin{bmatrix} 1 & 0 & 2 \\ -1 & 3 & 1 \end{bmatrix}, \qquad W = \begin{bmatrix} 2 & -1 \\ 0 & 1 \\ 1 & 3 \end{bmatrix}, \qquad b = \begin{bmatrix} -2 & 1 \end{bmatrix}$

The layer computes

$Z = XW + b, \qquad H = \mathrm{ReLU}(Z)$

where the layer has one bias per hidden unit, so the same row vector $b$ is added to every row of $XW$, and $\mathrm{ReLU}(z) = \max(0, z)$ is applied to each entry separately.

What is the hidden activation matrix $H$?