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: "generative adversarial self imitation learning"
  Abstract: This paper explores a simple regularizer for reinforcement learning by proposing Generative Adversarial Self-Imitation Learning (GASIL), which encourages the agent to imitate past good trajectories via generative adversarial imitation learning framework. Instead of directly maximizing rewards, GASIL focuses on reproducing past good trajectories, which can potentially make long-term credit assignment easier when rewards are sparse and delayed. GASIL can be easily combined with any policy gradient objective by using GASIL as a learned shaped reward function. Our experimental results show that GASIL improves the performance of proximal policy optimization on 2D Point Mass and MuJoCo environments with delayed reward and stochastic dynamics.

Connected through hyperedge HE-1 (induced by source paper "machine learning approaches for motor learning a short review"):
  with members:
    - "human motion trajectory prediction a survey"
    - "vred a position velocity recurrent encoder decoder for human motion prediction"
    - "motion prediction with recurrent neural network dynamical models and trajectory optimization"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "convolutional sequence to sequence model for human dynamics"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "deep online learning via meta learning continual adaptation for model based rl"
    - "one shot imitation from observing humans via domain adaptive meta learning"

Connected through hyperedge HE-2 (induced by source paper "self imitation learning via trajectory conditioned policy for hard exploration tasks"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "behaviour suite for reinforcement learning"
    - "skew fit state covering self supervised reinforcement learning"
    - "making efficient use of demonstrations to solve hard exploration problems"
    - "exploration by random network distillation"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"
    - "playing hard exploration games by watching youtube"
    - "zero shot visual imitation"

Connected through hyperedge HE-3 (induced by source paper "hindsight generative adversarial imitation learning"):
  with members:
    - "adversarial imitation via variational inverse reinforcement learning"
    - "zero shot visual imitation"
    - "curiosity driven experience prioritization via density estimation"
    - "self imitation learning"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "generative adversarial imitation from observation"
    - "energy based hindsight experience prioritization"

Connected through hyperedge HE-4 (induced by source paper "sequence modeling of temporal credit assignment for episodic reinforcement learning"):
  with members:
    - "optimizing agent behavior over long time scales by transporting value"
    - "on learning intrinsic rewards for policy gradient methods"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evolved policy gradients"
    - "rudder return decomposition for delayed rewards"
    - "hindsight experience replay"
    - "molecular de novo design through deep reinforcement learning"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "google research football a novel reinforcement learning environment"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "emergent coordination through competition"
    - "deepmind control suite"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "soft actor critic algorithms and applications"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "distributed prioritized experience replay"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "randomized adversarial imitation learning for autonomous driving"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "automated speed and lane change decision making using deep reinforcement learning"
    - "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning"
    - "deep reinforcement learning that matters"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "towards generalization and simplicity in continuous control"
    - "optiongan learning joint reward policy options using generative adversarial inverse reinforcement learning"

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)