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 generative adversarial imitation learning"
  Abstract: Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal. However, most existing approaches are not applicable in multi-agent settings due to the existence of multiple (Nash) equilibria and non-stationary environments. We propose a new framework for multi-agent imitation learning for general Markov games, where we build upon a generalized notion of inverse reinforcement learning. We further introduce a practical multi-agent actor-critic algorithm with good empirical performance. Our method can be used to imitate complex behaviors in high-dimensional environments with multiple cooperative or competing agents.

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 inverse reinforcement learning for general sum stochastic games"
    - "multi agent imitation learning for driving simulation"

Connected through hyperedge HE-2 (induced by source paper "scalable multi agent inverse reinforcement learning via actor attention critic"):
  with members:
    - "modeling social group communication with multi agent imitation learning"
    - "multi agent adversarial inverse reinforcement learning"
    - "pytorch an imperative style high performance deep learning library"
    - "actor attention critic for multi agent reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning"
    - "counterfactual multi agent policy gradients"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-3 (induced by source paper "how do we move learning to simulate with system dynamics"):
  with members:
    - "challenges of real world reinforcement learning"
    - "peeking into the future predicting future person activities and locations in videos"
    - "inferring metapopulation propagation network for intra city epidemic control and prevention"
    - "multi agent imitation learning for driving simulation"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-4 (induced by source paper "multi objective multi agent decision making a utility based analysis and survey"):
  with members:
    - "emergent coordination through competition"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "resource abstraction for reinforcement learning in multiagent congestion problems"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "dynamic weights in multi objective deep reinforcement learning"
    - "ordered preference elicitation strategies for supporting multi objective decision making"

Connected through hyperedge HE-5 (induced by source paper "independent generative adversarial self imitation learning in cooperative multiagent systems"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "self imitation learning"
    - "towards cooperation in sequential prisoner s dilemmas a deep multiagent reinforcement learning approach"
    - "learning from demonstration in the wild"
    - "multi agent imitation learning for driving simulation"
    - "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning"
    - "counterfactual multi agent policy gradients"
    - "guided deep reinforcement learning for swarm systems"

Connected through hyperedge HE-6 (induced by source paper "multi agent adversarial inverse reinforcement learning"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "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"
    - "inverse reward design"

Connected through hyperedge HE-7 (induced by source paper "sample efficient adversarial imitation learning from observation"):
  with members:
    - "imitation learning from video by leveraging proprioception"
    - "recent advances in imitation learning from observation"
    - "generative adversarial imitation from observation"
    - "behavioral cloning from observation"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "third person imitation learning"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-8 (induced by source paper "simulating emergent properties of human driving behavior using multi agent reward augmented imitation learning"):
  with members:
    - "multi agent imitation learning for driving simulation"
    - "imitating driver behavior with generative adversarial networks"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "rail risk averse imitation learning"
    - "closed loop policies for operational tests of safety critical systems"
    - "generative adversarial imitation learning"
    - "trust region policy optimization"
    - "imitation learning with a value based prior"

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.LG(Machine Learning)