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 "the complexity of nash equilibria as revealed by data"):
  Abstract: In this paper we initiate the study of the computational complexity of Nash equilibria in bimatrix games that are specified via data.  This direction is motivated by an attempt to connect the emerging work on the computational complexity of Nash equilibria with the perspective of revealed preference theory, where inputs are data about observed behavior, rather than explicit payoffs.  Our results draw such connections for large classes of data sets, and provide a formal basis for studying these connections more generally. In particular,  we derive three structural conditions that are sufficient to ensure that a data set is both consistent with Nash equilibria and that the observed equilibria could have been computed effciently: (i) small dimensionality of  the  observed  strategies, (ii) small support  size of  the  observed  strategies,  and  (iii)  small chromatic  number of the data set.  Key to these results is a connection between data sets and the player rank of a game, defined to be the minimum rank of the payoff matrices of the players. We complement our results by constructing data sets that require rationalizing games to have high player rank, which suggests that computational constraints may be important empirically as well.

Includes members:
  Member "constant rank bimatrix games are ppad hard":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "consenus halving does it ever get easier")
  Member "computational rationalization the inverse equilibrium problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi agent interactions modeling with correlated policies")
  Member "the empirical implications of rank in bimatrix games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recovering games from perturbed equilibrium observations using convex optimization")
  Member "efficiently learning from revealed preference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "grinding the space learning to classify against strategic agents")
  Member "bilinear games polynomial time algorithms for rank based subclasses":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "computing stackelberg equilibria of large general sum games")
  Member "settling the complexity of arrow debreu equilibria in markets with additively separable utilities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the complexity of interactively learning a stable matching by trial and error")
  Member "settling the complexity of computing two player nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)