human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "computing nash equilibria of action graph games"
  Abstract: Action-graph games (AGGs) are a fully expressive game representation which can compactly express both strict and context-specific independence between players' utility functions. Actions are represented as nodes in a graph G, and the payoff to an agent who chose the action s depends only on the numbers of other agents who chose actions connected to s. We present algorithms for computing both symmetric and arbitrary equilibria of AGGs using a continuation method. We analyze the worst-case cost of computing the Jacobian of the payoff function, the exponential-time bottleneck step, and in all cases achieve exponential speedup. When the indegree of G is bounded by a constant and the game is symmetric, the Jacobian can be computed in polynomial time.

Connected through hyperedge HE-1 (induced by source paper "an empirical study on computing equilibria in polymatrix games"):
  with members:
    - "approximating nash equilibria in tree polymatrix games"
    - "from duels to battefields computing equilibria of blotto and other games"
    - "an empirical study of finding approximate equilibria in bimatrix games"
    - "coordination games on graphs"
    - "on the complexity of computing an equilibrium in combinatorial auctions"
    - "inapproximability of nash equilibrium"
    - "computing approximate nash equilibria in polymatrix games"
    - "the complexity of non monotone markets"

Connected through hyperedge HE-2 (induced by source paper "computational analysis of perfect information position auctions"):
  with members:
    - "graphical models for game theory"
    - "ranking and tradeoffs in sponsored search auctions"
    - "constrained automated mechanism design for infinite games of incomplete information"
    - "sponsored search auctions with markovian users"

Connected through hyperedge HE-3 (induced by source paper "temporal action graph games a new representation for dynamic games"):
  with members:
    - "computational analysis of perfect information position auctions"
    - "graphical models for game theory"
    - "context specific independence in bayesian networks"
    - "evaluating influence diagrams using limids"
    - "a continuation method for nash equilibria in structured games"
    - "exploiting contextual independence in probabilistic inference"

Connected through hyperedge HE-4 (induced by source paper "learning and solving many player games through a cluster based representation"):
  with members:
    - "graphical models for game theory"
    - "efficient nash computation in large population games with bounded influence"
    - "computing equilibria in anonymous games"

Connected through hyperedge HE-5 (induced by source paper "a general framework for computing optimal correlated equilibria in compact games"):
  with members:
    - "graphical models for game theory"
    - "polynomial time computation of exact correlated equilibrium in compact games"
    - "a continuation method for nash equilibria in structured games"

Connected through hyperedge HE-6 (induced by source paper "if more than analytical modeling is needed to predict real agents strategic interaction"):
  with members:
    - "graphical models for game theory"
    - "general game management agent"

Connected through hyperedge HE-7 (induced by source paper "polynomial time computation of exact correlated equilibrium in compact games"):
  with members:
    - "graphical models for game theory"
    - "exchangeable equilibria contradict exactness of the papadimitriou roughgarden algorithm"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-8 (induced by source paper "pure nash equilibria complete characterization of hard and easy graphical games"):
  with members:
    - "graphical models for game theory"
    - "pure nash equilibria hard and easy games"
    - "computing pure nash equilibria via markov random fields"

Question: Please tell me which class the center node 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)