  We study hypergraph clustering in the weighted $d$-uniform hypergraph
stochastic block model ($d$\textsf{-WHSBM}), where each edge consisting of $d$
nodes from the same community has higher expected weight than the edges
consisting of nodes from different communities. We propose a new hypergraph
clustering algorithm, called \textsf{CRTMLE}, and provide its performance
guarantee under the $d$\textsf{-WHSBM} for general parameter regimes. We show
that the proposed method achieves the order-wise optimal or the best existing
results for approximately balanced community sizes. Moreover, our results
settle the first recovery guarantees for growing number of clusters of
unbalanced sizes. Involving theoretical analysis and empirical results, we
demonstrate the robustness of our algorithm against the unbalancedness of
community sizes or the presence of outlier nodes.
