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 below two thirds"
  Abstract: In an epsilon-Nash equilibrium, a player can gain at most epsilon by changing his behaviour. Recent work has addressed the question of how best to compute epsilon-Nash equilibria, and for what values of epsilon a polynomial-time algorithm exists. An epsilon-well-supported Nash equilibrium (epsilon-WSNE) has the additional requirement that any strategy that is used with non-zero probability by a player must have payoff at most epsilon less than the best response. A recent algorithm of Kontogiannis and Spirakis shows how to compute a 2/3-WSNE in polynomial time, for bimatrix games. Here we introduce a new technique that leads to an improvement to the worst-case approximation guarantee.

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"
    - "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"
    - "on the communication complexity of approximate nash equilibria"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-2 (induced by source paper "computing constrained approximate equilibria in polymatrix games"):
  with members:
    - "distributed methods for computing approximate equilibria"
    - "fptas for mixed strategy nash equilibria in tree graphical games and their generalizations"
    - "approximating nash equilibria in tree polymatrix games"
    - "an empirical study on computing equilibria in polymatrix games"
    - "inapproximability results for approximate nash equilibria"
    - "finding any nontrivial coarse correlated equilibrium is hard"
    - "inapproximability of nash equilibrium"
    - "computing approximate nash equilibria in polymatrix games"

Connected through hyperedge HE-3 (induced by source paper "inapproximability results for approximate nash equilibria"):
  with members:
    - "distributed methods for computing approximate equilibria"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "am with multiple merlins"
    - "inapproximability of np complete variants of nash equilibrium"

Connected through hyperedge HE-4 (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"
    - "polylogarithmic supports are required for approximate well supported nash equilibria below 2 3"
    - "combining local search techniques and path following for bimatrix games"
    - "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-5 (induced by source paper "large supports are required for well supported nash equilibria"):
  with members:
    - "polylogarithmic supports are required for approximate well supported nash equilibria below 2 3"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-6 (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 in symmetric bimatrix games"
    - "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-7 (induced by source paper "approximate well supported nash equilibria in symmetric bimatrix games"):
  with members:
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-8 (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"
    - "on the communication complexity of approximate nash equilibria"
    - "query complexity of approximate nash equilibria"
    - "query complexity of correlated equilibrium"
    - "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)