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 "constant rank bimatrix games are ppad hard"):
  Abstract: The rank of a bimatrix game (A,B) is defined as rank(A+B). Computing a Nash equilibrium (NE) of a rank-$0$, i.e., zero-sum game is equivalent to linear programming (von Neumann'28, Dantzig'51). In 2005, Kannan and Theobald gave an FPTAS for constant rank games, and asked if there exists a polynomial time algorithm to compute an exact NE. Adsul et al. (2011) answered this question affirmatively for rank-$1$ games, leaving rank-2 and beyond unresolved. #R##N#In this paper we show that NE computation in games with rank $\ge 3$, is PPAD-hard, settling a decade long open problem. Interestingly, this is the first instance that a problem with an FPTAS turns out to be PPAD-hard. Our reduction bypasses graphical games and game gadgets, and provides a simpler proof of PPAD-hardness for NE computation in bimatrix games. In addition, we get: #R##N#* An equivalence between 2D-Linear-FIXP and PPAD, improving a result by Etessami and Yannakakis (2007) on equivalence between Linear-FIXP and PPAD. #R##N#* NE computation in a bimatrix game with convex set of Nash equilibria is as hard as solving a simple stochastic game. #R##N#* Computing a symmetric NE of a symmetric bimatrix game with rank $\ge 6$ is PPAD-hard. #R##N#* Computing a (1/poly(n))-approximate fixed-point of a (Linear-FIXP) piecewise-linear function is PPAD-hard. #R##N#The status of rank-$2$ games remains unresolved.

Includes members:
  Member "settling some open problems on 2 player symmetric nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the computational complexity of decision problems about multi player nash equilibria")
  Member "a direct reduction of ppad lemke verified linear complementarity problems to bimatrix games":
    also connected through other hyperedges:
      (none)
  Member "on oblivious ptas s for nash equilibrium":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "private hypothesis selection")
  Member "bilinear games polynomial time algorithms for rank based subclasses":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "computing stackelberg equilibria of large general sum games")
  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 "settling the complexity of arrow debreu equilibria in markets with additively separable utilities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the complexity of interactively learning a stable matching by trial and error")
  Member "reducibility among fractional stability problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the classes ppa k existence from arguments modulo k")
  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)