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 "an empirical study of finding approximate equilibria in bimatrix games"):
  Abstract: While there have been a number of studies about the efficacy of methods to find exact Nash equilibria in bimatrix games, there has been little empirical work on finding approximate Nash equilibria. Here we provide such a study that compares a number of approximation methods and exact methods. In particular, we explore the trade-off between the quality of approximate equilibrium and the required running time to find one. We found that the existing library GAMUT, which has been the de facto standard that has been used to test exact methods, is insufficient as a test bed for approximation methods since many of its games have pure equilibria or other easy-to-find good approximate equilibria. We extend the breadth and depth of our study by including new interesting families of bimatrix games, and studying bimatrix games upto size $2000 \times 2000$. Finally, we provide new close-to-worst-case examples for the best-performing algorithms for finding approximate Nash equilibria.

Includes members:
  Member "unit vector games":
    also connected through other hyperedges:
      (none)
  Member "ranking games that have competitiveness based strategies":
    also connected through other hyperedges:
      (none)
  Member "polylogarithmic supports are required for approximate well supported nash equilibria below 2 3":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "settling the complexity of computing approximate two player nash equilibria")
  Member "combining local search techniques and path following for bimatrix games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "computational results for extensive form adversarial team games")
  Member "approximate well supported nash equilibria below two thirds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed methods for computing approximate equilibria")
  Member "the complexity of the homotopy method equilibrium selection and lemke howson solutions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a generalized training approach for multiagent learning")
  Member "an experimental analysis of lemke howson algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "smoothed efficient algorithms and reductions for network coordination games")
  Member "settling the complexity of computing two player nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")

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)