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: "analyzing incentives for protocol compliance in complex domains a case study of introduction based routing"
  Abstract: Formal analyses of incentives for compliance with network protocols often appeal to gametheoretic models and concepts. Applications of game-theoretic analysis to network security have generally been limited to highly stylized models, where simplified environments enable tractable study of key strategic variables. We propose a simulation-based approach to gametheoretic analysis of protocol compliance, for scenarios with large populations of agents and large policy spaces. We define a general procedure for systematically exploring a structured policy space, directed expressly to resolve the qualitative classification of equilibrium behavior as compliant or non-compliant. The techniques are illustrated and exercised through an extensive case study analyzing compliance incentives for introduction-based routing. We find that the benefits of complying with the protocol are particularly strong for nodes subject to attack, and the overall compliance level achieved in equilibrium, while not universal, is sufficient to support the desired security goals of the protocol.

Connected through hyperedge HE-1 (induced by source paper "alpha rank multi agent evaluation by evolution"):
  with members:
    - "emergent coordination through competition"
    - "a generalised method for empirical game theoretic analysis"
    - "re evaluating evaluation"
    - "the exact computational complexity of evolutionarily stable strategies"
    - "learning dynamics and the co evolution of competing sexual species"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "deepstack expert level artificial intelligence in heads up no limit poker"

Connected through hyperedge HE-2 (induced by source paper "multiagent evaluation under incomplete information"):
  with members:
    - "emergent coordination through competition"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "a generalised method for empirical game theoretic analysis"
    - "re evaluating evaluation"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "population based training of neural networks"
    - "giraffe using deep reinforcement learning to play chess"
    - "pagerank optimization by edge selection"

Connected through hyperedge HE-3 (induced by source paper "learning equilibria of simulation based games"):
  with members:
    - "a generalised method for empirical game theoretic analysis"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "aivat a new variance reduction technique for agent evaluation in imperfect information games"
    - "learning equilibria of games via payoff queries"
    - "query complexity of approximate nash equilibria"

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)