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: "approximate well supported nash equilibria in symmetric bimatrix games"
  Abstract: The $\varepsilon$-well-supported Nash equilibrium is a strong notion of approximation of a Nash equilibrium, where no player has an incentive greater than $\varepsilon$ to deviate from any of the pure strategies that she uses in her mixed strategy. The smallest constant $\varepsilon$ currently known for which there is a polynomial-time algorithm that computes an $\varepsilon$-well-supported Nash equilibrium in bimatrix games is slightly below $2/3$. In this paper we study this problem for symmetric bimatrix games and we provide a polynomial-time algorithm that gives a $(1/2+\delta)$-well-supported Nash equilibrium, for an arbitrarily small positive constant $\delta$.

Connected through hyperedge HE-1 (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"
    - "finding approximate nash equilibria of bimatrix games via payoff queries"
    - "learning equilibria of games via payoff queries"
    - "on the communication complexity of approximate nash equilibria"
    - "approximate well supported nash equilibria below two thirds"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-2 (induced by source paper "computing approximate nash equilibria in polymatrix games"):
  with members:
    - "an empirical study of finding approximate equilibria in bimatrix games"
    - "inapproximability of nash equilibrium"
    - "approximate well supported nash equilibria below two thirds"
    - "the complexity of non monotone markets"
    - "inapproximability of np complete variants of nash equilibrium"
    - "a direct reduction from k player to 2 player approximate nash equilibrium"
    - "approximate equilibria in games with few players"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-3 (induced by source paper "approximate well supported nash equilibria below two thirds"):
  with members:
    - "an empirical study of finding approximate equilibria in bimatrix games"
    - "polylogarithmic supports are required for approximate well supported nash equilibria below 2 3"
    - "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)