Complexity classes like P, NP, NP-Hard, and NP-Complete can be defined as sets of problems based on the existence of algorithms with specific time complexities. The discussion highlights the challenge of expressing these classes purely in set-theoretic terms, as definitions may vary based on foundational concepts like decision problems and Turing computation. While it's possible to represent these classes as sets, doing so may not capture their complexity or practical significance. The conversation suggests that bridging the gap between complexity classes and basic set notions requires a nuanced approach, potentially involving coding and complexity measures. Ultimately, the complexity of these definitions can lead to confusion and may seem meaningless without a proper context.