Is the k-center problem part of machine learning or computational geometry?
Where is the "learning"? Where is the "training", and "becoming better"?

It's an optimization problem, but optimization is everywhere.

Fits computational geometry much better than ML.

But as a rough guideline, check which books discuss this well. Can you name a book that belongs into machine learning that discusses this is sufficient depth?