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: "eth hardness for signaling in symmetric zero sum games"
  Abstract: We prove that, assuming the exponential time hypothesis, finding an \epsilon-approximately optimal symmetric signaling scheme in a two-player zero-sum game requires quasi-polynomial time. This is tight by [CCDEHT'15] and resolves an open question of [Dughmi'14]. We also prove that finding a multiplicative approximation is NP-hard.

Connected through hyperedge HE-1 (induced by source paper "inapproximability of vc dimension and littlestone s dimension"):
  with members:
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "detecting communities is hard and counting them is even harder"
    - "on the approximability of partial vc dimension"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "eth hardness for densest k subgraph with perfect completeness"
    - "complexity theoretic limitations on learning halfspaces"
    - "hardness results for signaling in bayesian zero sum and network routing games"

Connected through hyperedge HE-2 (induced by source paper "settling the complexity of computing approximate two player nash equilibria"):
  with members:
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "detecting communities is hard and counting them is even harder"
    - "approximating nash equilibria in tree polymatrix games"
    - "the query complexity of correlated equilibria"
    - "communication complexity of approximate nash equilibria"
    - "inapproximability results for approximate nash equilibria"
    - "well supported versus approximate nash equilibria query complexity of large games"
    - "near optimal hardness results for signaling in bayesian games"

Connected through hyperedge HE-3 (induced by source paper "almost polynomial ratio eth hardness of approximating densest k subgraph"):
  with members:
    - "settling the complexity of computing approximate two player nash equilibria"
    - "inapproximability results for approximate nash equilibria"
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "partitioning a graph into small pieces with applications to path transversal"
    - "combining traditional marketing and viral marketing with amphibious influence maximization"
    - "eth hardness for densest k subgraph with perfect completeness"
    - "approximating dense max 2 csps"
    - "finding a collective set of items from proportional multirepresentation to group recommendation"

Connected through hyperedge HE-4 (induced by source paper "detecting communities is hard and counting them is even harder"):
  with members:
    - "community recovery in graphs with locality"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "how robust are reconstruction thresholds for community detection"
    - "hardness results for signaling in bayesian zero sum and network routing games"
    - "mixture selection mechanism design and signaling"
    - "am with multiple merlins"
    - "axioms for graph clustering quality functions"
    - "finding endogenously formed communities"

Connected through hyperedge HE-5 (induced by source paper "near optimal hardness results for signaling in bayesian games"):
  with members:
    - "mixture selection mechanism design and signaling"
    - "on the hardness of signaling"
    - "am with multiple merlins"
    - "constrained signaling in auction design"
    - "revenue maximization via hiding item attributes"
    - "send mixed signals earn more work less"

Connected through hyperedge HE-6 (induced by source paper "hardness results for signaling in bayesian zero sum and network routing games"):
  with members:
    - "mixture selection mechanism design and signaling"
    - "on the hardness of signaling"
    - "am with multiple merlins"
    - "constrained signaling in auction design"
    - "revenue maximization via hiding item attributes"
    - "send mixed signals earn more work less"

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)