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 computational problems about nash equilibria in symmetric win lose games"):
  Abstract: We revisit the complexity of deciding, given a {\it bimatrix game,} whether it has a {\it Nash equilibrium} with certain natural properties; such decision problems were early known to be ${\mathcal{NP}}$-hard~\cite{GZ89}. We show that ${\mathcal{NP}}$-hardness still holds under two significant restrictions in simultaneity: the game is {\it win-lose} (that is, all {\it utilities} are $0$ or $1$) and {\it symmetric}. To address the former restriction, we design win-lose {\it gadgets} and a win-lose reduction; to accomodate the latter restriction, we employ and analyze the classical {\it ${\mathsf{GHR}}$-symmetrization}~\cite{GHR63} in the win-lose setting. Thus, {\it symmetric win-lose bimatrix games} are as complex as general bimatrix games with respect to such decision problems. As a byproduct of our techniques, we derive hardness results for search, counting and parity problems about Nash equilibria in symmetric win-lose bimatrix games.

Includes members:
  Member "the complexity of equilibria for risk modeling valuations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on equilibrium computation in biased games with quadratic penalties")
  Member "settling some open problems on 2 player symmetric nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the computational complexity of decision problems about multi player nash equilibria")
  Member "some tractable win lose games":
    also connected through other hyperedges:
      (none)
  Member "settling the complexity of computing two player nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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.CC(Computational Complexity)