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 "computing approximate nash equilibria in polymatrix games"):
  Abstract: In an $\epsilon$-Nash equilibrium, a player can gain at most $\epsilon$ by unilaterally changing his behaviour. For two-player (bimatrix) games with payoffs in $[0,1]$, the best-known$\epsilon$ achievable in polynomial time is 0.3393. In general, for $n$-player games an $\epsilon$-Nash equilibrium can be computed in polynomial time for an $\epsilon$ that is an increasing function of $n$ but does not depend on the number of strategies of the players. For three-player and four-player games the corresponding values of $\epsilon$ are 0.6022 and 0.7153, respectively. Polymatrix games are a restriction of general $n$-player games where a player's payoff is the sum of payoffs from a number of bimatrix games. There exists a very small but constant $\epsilon$ such that computing an $\epsilon$-Nash equilibrium of a polymatrix game is \PPAD-hard. Our main result is that a $(0.5+\delta)$-Nash equilibrium of an $n$-player polymatrix game can be computed in time polynomial in the input size and $\frac{1}{\delta}$. Inspired by the algorithm of Tsaknakis and Spirakis, our algorithm uses gradient descent on the maximum regret of the players. We also show that this algorithm can be applied to efficiently find a $(0.5+\delta)$-Nash equilibrium in a two-player Bayesian game.

Includes members:
  Member "an empirical study of finding approximate equilibria in bimatrix games":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed methods for computing approximate equilibria")
  Member "inapproximability of nash equilibrium":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the complexity of interactively learning a stable matching by trial and error")
  Member "approximate well supported nash equilibria in symmetric bimatrix games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed methods for computing approximate equilibria")
  Member "approximate well supported nash equilibria below two thirds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed methods for computing approximate equilibria")
  Member "the complexity of non monotone markets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "consenus halving does it ever get easier")
  Member "inapproximability of np complete variants of nash equilibrium":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reducibility and computational lower bounds for problems with planted sparse structure")
  Member "a direct reduction from k player to 2 player approximate nash equilibrium":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an empirical study on computing equilibria in polymatrix games")
  Member "approximate equilibria in games with few players":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an empirical study on computing equilibria in polymatrix games")

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)