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 "fast algorithms for rank 1 bimatrix games"):
  Abstract: The rank of a bimatrix game is the matrix rank of the sum of the two payoff matrices. For a game of rank k, the set of its Nash equilibria is the intersection of a generically one-dimensional set of equilibria of parameterized games of rank k-1 with a hyperplane. We comprehensively analyze games of rank one. They are economonically more interesting than zero-sum games (which have rank zero), but are nearly as easy to solve. One equilibrium of a rank-1 game can be found in polynomial time. All equilibria of a rank-1 game can be found by path-following, which finds only one equilibrium of a bimatrix game. The number of equilibria of a rank-1 game may be exponential. We also present a new rank-preserving homeomorphism between bimatrix games and their equilibrium correspondence.

Includes members:
  Member "a friendly smoothed analysis of the simplex method":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "smoothed analysis of order types")
  Member "constant rank bimatrix games are ppad hard":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "consenus halving does it ever get easier")
  Member "rank 1 games with exponentially many nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "game theory explorer software for the applied game theorist")
  Member "enumerating the nash equilibria of rank 1 games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rank reduction in bimatrix games")
  Member "settling the complexity of computing two player nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improper learning for non stochastic control")

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)