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 equilibria of games via payoff queries"):
  Abstract: A recent body of experimental literature has studied empirical game-theoretical analysis, in which we have partial knowledge of a game, consisting of observations of a subset of the pure-strategy profiles and their associated payoffs to players. The aim is to find an exact or approximate Nash equilibrium of the game, based on these observations. It is usually assumed that the strategy profiles may be chosen in an on-line manner by the algorithm. We study a corresponding computational learning model, and the query complexity of learning equilibria for various classes of games. We give basic results for bimatrix and graphical games. Our focus is on symmetric network congestion games. For directed acyclic networks, we can learn the cost functions (and hence compute an equilibrium) while querying just a small fraction of pure-strategy profiles. For the special case of parallel links, we have the stronger result that an equilibrium can be identified while only learning a small fraction of the cost values.

Includes members:
  Member "the query complexity of correlated equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "near optimal communication lower bounds for approximate nash equilibria")
  Member "finding approximate nash equilibria of bimatrix games via payoff queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed methods for computing approximate equilibria")
  Member "graphical models for game theory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "on the communication complexity of approximate nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the existence of pareto efficient and envy free allocations")
  Member "query complexity of approximate nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "query complexity of correlated equilibrium":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "settling the complexity of computing approximate two player nash equilibria")
  Member "approximate well supported nash equilibria below two thirds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed methods for computing approximate equilibria")
  Member "on the complexity of trial and error":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the dependent doors problem an investigation into sequential decisions without feedback")

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)