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: "random expert distillation imitation learning via expert policy support estimation"
  Abstract: We consider the problem of imitation learning from a finite set of expert trajectories, without access to reinforcement signals. The classical approach of extracting the expert's reward function via inverse reinforcement learning, followed by reinforcement learning is indirect and may be computationally expensive. Recent generative adversarial methods based on matching the policy distribution between the expert and the agent could be unstable during training. We propose a new framework for imitation learning by estimating the support of the expert policy to compute a fixed reward function, which allows us to re-frame imitation learning within the standard reinforcement learning setting. We demonstrate the efficacy of our reward function on both discrete and continuous domains, achieving comparable or better performance than the state of the art under different reinforcement learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "universal value density estimation for imitation learning and goal conditioned reinforcement learning"):
  with members:
    - "sqil imitation learning via reinforcement learning with sparse rewards"
    - "generative predecessor models for sample efficient imitation learning"
    - "is the policy gradient a gradient"
    - "sqil imitation learning via regularized behavioral cloning"
    - "visual hindsight experience replay"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-2 (induced by source paper "support weighted adversarial imitation learning"):
  with members:
    - "generative predecessor models for sample efficient imitation learning"
    - "exploration by random network distillation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-3 (induced by source paper "biologically inspired architectures for sample efficient deep reinforcement learning"):
  with members:
    - "weight agnostic neural networks"
    - "scattering networks for hybrid representation learning"
    - "when to use parametric models in reinforcement learning"
    - "model based reinforcement learning for atari"
    - "kymatio scattering transforms in python"
    - "generative networks as inverse problems with scattering transforms"
    - "playing atari with six neurons"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "sqil imitation learning via reinforcement learning with sparse rewards"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "the gan landscape losses architectures regularization and normalization"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning"
    - "where do you think you re going inferring beliefs about dynamics from behavior"
    - "recurrent world models facilitate policy evolution"
    - "deep reinforcement learning that matters"

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)