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 "query complexity of approximate nash equilibria"):
  Abstract: We study the query complexity of approximate notions of Nash equilibrium in games with a large number of players $n$. Our main result states that for $n$-player binary-action games and for constant $\varepsilon$, the query complexity of an $\varepsilon$-well-supported Nash equilibrium is exponential in $n$. One of the consequences of this result is an exponential lower bound on the rate of convergence of adaptive dynamics to approxiamte Nash equilibrium.

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 "inapproximability of nash equilibrium":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the complexity of interactively learning a stable matching by trial and error")
  Member "computational complexity of approximate nash equilibrium in large games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "settling the complexity of computing approximate two player nash equilibria")
  Member "finding approximate nash equilibria of bimatrix games via payoff queries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed methods for computing approximate equilibria")
  Member "learning equilibria of games via payoff queries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiagent evaluation under incomplete information")
  Member "approximate nash equilibria via sampling":
    also connected through other hyperedges:
      (none)
  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 "weak calibration is computationally hard":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reliable confidence estimation via online learning")

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)