human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning and efficiency in games with dynamic population"):
  Abstract: We study the quality of outcomes in repeated games when the population of players is dynamically changing and participants use learning algorithms to adapt to the changing environment. Game theory classically considers Nash equilibria of one-shot games, while in practice many games are players repeatedly, and in such games players often use algorithmic tools to learn to play in the given environment. Learning in repeated games has only been studied when the population playing the game is stable over time. #R##N#We analyze efficiency of repeated games in dynamically changing environments, motivated by application domains such as packet routing and Internet ad-auctions. We prove that, in many classes of games, if players choose their strategies in a way that guarantees low adaptive regret, then high social welfare is ensured, even under very frequent changes. This result extends previous work, which showed high welfare for learning outcomes in stable environments. A main technical tool for our analysis is the existence of a solution to the welfare maximization problem that is both close to optimal and relatively stable over time. Such a solution serves as a benchmark in the efficiency analysis of learning outcomes. We show that such stable and near-optimal solutions exist for many problems, even in cases when the exact optimal solution can be very unstable. We develop direct techniques to show the existence of a stable solution in some classes of games. Further, we show that a sufficient condition for the existence of stable solutions is the existence of a differentially private algorithm for the welfare maximization problem. We demonstrate our techniques by focusing on three classes of games as examples: simultaneous item auctions, bandwidth allocation mechanisms and congestion games.

Includes members:
  Member "private matchings and allocations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "differentially private call auctions and market impact")
  Member "jointly private convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differentially private call auctions and market impact")
  Member "achieving all with no parameters adaptive normalhedge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "competitive ratio versus regret minimization achieving the best of both worlds")
  Member "inducing approximately optimal flow using truthful mediators":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differentially private call auctions and market impact")
  Member "private pareto optimal exchange":
    also connected through other hyperedges:
      (none)
  Member "mirror descent meets fixed share and feels no regret":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "minimizing dynamic regret and adaptive regret simultaneously")
  Member "bayesian games and the smoothness framework":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fair allocation of indivisible goods improvement and generalization")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "locally private hypothesis selection")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.GT(Computer Science and Game Theory)