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: "polylogarithmic supports are required for approximate well supported nash equilibria below 2 3"
  Abstract: In an epsilon-approximate Nash equilibrium, a player can gain at most epsilon in expectation by unilateral deviation. An epsilon well-supported approximate Nash equilibrium has the stronger requirement that every pure strategy used with positive probability must have payoff within epsilon of the best response payoff. Daskalakis, Mehta and Papadimitriou conjectured that every win-lose bimatrix game has a 2/3-well-supported Nash equilibrium that uses supports of cardinality at most three. Indeed, they showed that such an equilibrium will exist subject to the correctness of a graph-theoretic conjecture. Regardless of the correctness of this conjecture, we show that the barrier of a 2/3 payoff guarantee cannot be broken with constant size supports; we construct win-lose games that require supports of cardinality at least Omega((log n)^(1/3)) in any epsilon-well supported equilibrium with epsilon   0; thus, the polylogarithmic cardinality bound presented cannot be greatly improved. We also show that for any delta > 0, there exist win-lose games for which no pair of strategies with support sizes at most two is a (1-delta)-well-supported Nash equilibrium. In contrast, every bimatrix game with payoffs in [0,1] has a 1/2-approximate Nash equilibrium where the supports of the players have cardinality at most two.

Connected through hyperedge HE-1 (induced by source paper "settling the complexity of computing approximate two player nash equilibria"):
  with members:
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "detecting communities is hard and counting them is even harder"
    - "approximating nash equilibria in tree polymatrix games"
    - "the query complexity of correlated equilibria"
    - "communication complexity of approximate nash equilibria"
    - "inapproximability results for approximate nash equilibria"
    - "well supported versus approximate nash equilibria query complexity of large games"
    - "near optimal hardness results for signaling in bayesian games"

Connected through hyperedge HE-2 (induced by source paper "an empirical study of finding approximate equilibria in bimatrix games"):
  with members:
    - "unit vector games"
    - "ranking games that have competitiveness based strategies"
    - "combining local search techniques and path following for bimatrix games"
    - "approximate well supported nash equilibria below two thirds"
    - "the complexity of the homotopy method equilibrium selection and lemke howson solutions"
    - "an experimental analysis of lemke howson algorithm"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-3 (induced by source paper "large supports are required for well supported nash equilibria"):
  with members:
    - "approximate well supported nash equilibria below two thirds"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-4 (induced by source paper "finding approximate nash equilibria of bimatrix games via payoff queries"):
  with members:
    - "distributed methods for computing approximate equilibria"
    - "the query complexity of correlated equilibria"
    - "well supported versus approximate nash equilibria query complexity of large games"
    - "query complexity of approximate equilibria in anonymous games"
    - "learning equilibria of games via payoff queries"
    - "query complexity of approximate nash equilibria"
    - "query complexity of correlated equilibrium"
    - "approximate well supported nash equilibria below two thirds"

Connected through hyperedge HE-5 (induced by source paper "approximate well supported nash equilibria below two thirds"):
  with members:
    - "an empirical study of finding approximate equilibria in bimatrix games"
    - "approximate well supported nash equilibria in symmetric bimatrix games"
    - "settling the complexity of computing two player nash equilibria"

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)