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: "sqil imitation learning via regularized behavioral cloning"
  Abstract: Learning to imitate expert behavior given action demonstrations containing high-dimensional, continuous observations and unknown dynamics is a difficult problem in robotic control. Simple approaches based on behavioral cloning (BC) suffer from state distribution shift, while more complex methods that generalize to out-of-distribution states can be difficult to use, since they typically involve adversarial optimization. We propose an alternative that combines the simplicity of BC with the robustness of adversarial imitation learning. The key insight is that under the maximum entropy model of expert behavior, BC corresponds to fitting a soft Q function that maximizes the likelihood of observed actions. This perspective suggests a way to regularize BC so that it generalizes to out-of-distribution states: combine the standard maximum-likelihood objective with a penalty on the soft Bellman error of the soft Q function. We show that this penalty term gives the agent an incentive to take actions that lead it back to demonstrated states when it encounters new states. Experiments show that our method outperforms BC and GAIL on a variety of image-based and low-dimensional environments in Box2D, Atari, and MuJoCo.

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"
    - "random expert distillation imitation learning via expert policy support estimation"
    - "generative predecessor models for sample efficient imitation learning"
    - "is the policy gradient a gradient"
    - "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 "discriminator soft actor critic without extrinsic rewards"):
  with members:
    - "improving generalization and stability of generative adversarial networks"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning for robotic manipulation using simulated locomotion demonstrations"):
  with members:
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "exploration by random network distillation"
    - "learning dexterous in hand manipulation"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "visual reinforcement learning with imagined goals"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "large scale study of curiosity driven learning"
    - "multi goal reinforcement learning challenging robotics environments and request for research"

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)