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 "communication complexity of approximate nash equilibria"):
  Abstract: For a constant $\epsilon$, we prove a poly(N) lower bound on the (randomized) communication complexity of $\epsilon$-Nash equilibrium in two-player NxN games. For n-player binary-action games we prove an exp(n) lower bound for the (randomized) communication complexity of $(\epsilon,\epsilon)$-weak approximate Nash equilibrium, which is a profile of mixed actions such that at least $(1-\epsilon)$-fraction of the players are $\epsilon$-best replying.

Includes members:
  Member "distributed methods for computing approximate equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "near optimal communication lower bounds for approximate nash equilibria")
  Member "the query complexity of correlated equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "near optimal communication lower bounds for approximate nash equilibria")
  Member "settling the complexity of computing approximate two player nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "well supported versus approximate nash equilibria query complexity of large games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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-6 (induced by source paper "settling the complexity of computing approximate two player nash equilibria")
  Member "learning equilibria of games via payoff queries":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multiagent evaluation under incomplete information")
  Member "on the communication complexity of approximate nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the existence of pareto efficient and envy free allocations")

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)