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: "multi agent inverse reinforcement learning for zero sum games"
  Abstract: In this paper we introduce a Bayesian framework for solving a class of problems termed Multi-agent Inverse Reinforcement Learning (MIRL). Compared to the well-known Inverse Reinforcement Learning (IRL) problem, MIRL is formalized in the context of a stochastic game rather than a Markov decision process (MDP). Games bring two primary challenges: First, the concept of optimality, central to MDPs, loses its meaning and must be replaced with a more general solution concept, such as the Nash equilibrium. Second, the non-uniqueness of equilibria means that in MIRL, in addition to multiple reasonable solutions for a given inversion model, there may be multiple inversion models that are all equally sensible approaches to solving the problem. We establish a theoretical foundation for competitive two-agent MIRL problems and propose a Bayesian optimization algorithm to solve the problem. We focus on the case of two-person zero-sum stochastic games, developing a generative model for the likelihood of unknown rewards of agents given observed game play assuming that the two agents follow a minimax bipolicy. As a numerical illustration, we apply our method in the context of an abstract soccer game. For the soccer game, we investigate relationships between the extent of prior information and the quality of learned rewards. Results suggest that covariance structure is more important than mean value in reward priors.

Connected through hyperedge HE-1 (induced by source paper "multi agent interactions modeling with correlated policies"):
  with members:
    - "a regularized opponent model with maximum entropy objective"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "multi agent adversarial inverse reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "learning policy representations in multiagent systems"
    - "multi agent generative adversarial imitation learning"
    - "multi agent inverse reinforcement learning for general sum stochastic games"

Connected through hyperedge HE-2 (induced by source paper "multi agent adversarial inverse reinforcement learning"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "multi agent generative adversarial imitation learning"
    - "multi agent inverse reinforcement learning for general sum stochastic games"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "coordinated multi agent imitation learning"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "infogail interpretable imitation learning from visual demonstrations"

Connected through hyperedge HE-3 (induced by source paper "a survey of inverse reinforcement learning challenges methods and progress"):
  with members:
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "learning to cooperate via policy search"
    - "computational rationalization the inverse equilibrium problem"
    - "continuous inverse optimal control with locally optimal examples"
    - "apprenticeship learning using inverse reinforcement learning and gradient methods"
    - "a framework for sequential planning in multi agent settings"
    - "the communicative multiagent team decision problem analyzing teamwork theories and models"
    - "reinforcement learning a survey"

Connected through hyperedge HE-4 (induced by source paper "comparison of multi agent and single agent inverse learning on a simulated soccer example"):
  with members:
    - "computational rationalization the inverse equilibrium problem"
    - "inverse reinforcement learning with gaussian process"
    - "multiagent learning in large anonymous games"
    - "a multiagent reinforcement learning algorithm with non linear dynamics"

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)