Machine Learning and the Continuum Hypothesis

It seems that we have two very different problems here: There is the PAC-learning problem from theoretical computer science discussing whether or not machines can learn certain functions. And there is the Continuum Problem asking whether there are infinite sets of a certain size. What does this have to do with each other? In 2019, a group of researchers, Ben-David et al., published an article entitled "Learnability can be undecidable" in Nature Machine Intelligence: We describe simple scenarios where learnability cannot be proved nor refuted using the standard axioms of mathematics. Our proof is based on the fact the continuum hypothesis cannot be proved nor refuted.