human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on the communication complexity of approximate nash equilibria"
  Abstract: We study the problem of computing approximate Nash equilibria of bimatrix games, in a setting where players initially know their own payoffs but not the payoffs of the other player. In order for a solution of reasonable quality to be found, some amount of communication needs to take place between the players. We are interested in algorithms where the communication is substantially less than the contents of a payoff matrix, for example logarithmic in the size of the matrix. When the communication is polylogarithmic in the number of strategies n, we show how to obtain epsilon-approximate Nash equilibria for epsilon approximately 0.438, and for well-supported approximate equilibria we obtain epsilon approximately 0.732. For one-way communication we show that epsilon=1/2 is achievable, but no constant improvement over 1/2 is possible, even with unlimited one-way communication. For well-supported equilibria, no value of epsilon less than 1 is achievable with one-way communication. When the players do not communicate at all, epsilon-Nash equilibria can be obtained for epsilon=3/4, and we also give a lower bound of slightly more than 1/2 on the lowest constant epsilon achievable.

Connected through hyperedge HE-1 (induced by source paper "on the existence of pareto efficient and envy free allocations"):
  with members:
    - "near optimal communication lower bounds for approximate nash equilibria"
    - "communication complexity of cake cutting"
    - "almost envy freeness with general valuations"
    - "approximating the nash social welfare with budget additive valuations"
    - "communication complexity of discrete fair division"
    - "convex program duality fisher markets and nash social welfare"
    - "computing equilibria in markets with budget additive utilities"
    - "nash social welfare for indivisible items under separable piecewise linear concave utilities"

Connected through hyperedge HE-2 (induced by source paper "distributed methods for computing approximate equilibria"):
  with members:
    - "settling the complexity of computing approximate two player nash equilibria"
    - "communication complexity of approximate nash equilibria"
    - "an empirical study of finding approximate equilibria in bimatrix games"
    - "approximate well supported nash equilibria in symmetric bimatrix games"
    - "finding approximate nash equilibria of bimatrix games via payoff queries"
    - "learning equilibria of games via payoff queries"
    - "approximate well supported nash equilibria below two thirds"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-3 (induced by source paper "near optimal communication lower bounds for approximate nash equilibria"):
  with members:
    - "distributed methods for computing approximate equilibria"
    - "the communication complexity of local search"
    - "complexity theory game theory and economics"
    - "communication complexity of correlated equilibrium in two player games"
    - "query to communication lifting for bpp"
    - "the query complexity of correlated equilibria"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "communication complexity of approximate nash equilibria"

Connected through hyperedge HE-4 (induced by source paper "communication complexity of correlated equilibrium in two player games"):
  with members:
    - "settling the complexity of computing approximate two player nash equilibria"
    - "finding any nontrivial coarse correlated equilibrium is hard"
    - "communication complexity for algorithm designers"
    - "inapproximability of nash equilibrium"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-5 (induced by source paper "communication complexity of approximate nash equilibria"):
  with members:
    - "distributed methods for computing approximate equilibria"
    - "the query complexity of correlated equilibria"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "well supported versus approximate nash equilibria query complexity of large games"
    - "inapproximability of nash equilibrium"
    - "computational complexity of approximate nash equilibrium in large games"
    - "learning equilibria of games via payoff queries"
    - "query complexity of approximate nash equilibria"

Connected through hyperedge HE-6 (induced by source paper "learning equilibria of games via payoff queries"):
  with members:
    - "the query complexity of correlated equilibria"
    - "finding approximate nash equilibria of bimatrix games via payoff queries"
    - "graphical models for game theory"
    - "query complexity of approximate nash equilibria"
    - "query complexity of correlated equilibrium"
    - "approximate well supported nash equilibria below two thirds"
    - "on the complexity of trial and error"
    - "on oblivious ptas s for nash equilibrium"

Question: Please tell me which class the center node 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)