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: "imitating latent policies from observation"
  Abstract: We describe a novel approach to imitation learning that infers latent policies directly from state observations. We introduce a method that characterizes the causal effects of unknown actions on observations while simultaneously predicting their likelihood. We then outline an action alignment procedure that leverages a small amount of environment interactions to determine a mapping between latent and real-world actions. We show that this corrected labeling can be used for imitating the observed behavior, even though no expert actions are given. We evaluate our approach within classic control and photo-realistic visual environments and demonstrate that it performs well when compared to standard approaches.

Connected through hyperedge HE-1 (induced by source paper "learning predictive models from observation and interaction"):
  with members:
    - "experience embedded visual foresight"
    - "imitation learning from video by leveraging proprioception"
    - "improved conditional vrnns for video prediction"
    - "learning robotic manipulation through visual planning and acting"
    - "model based reinforcement learning for atari"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "adversarial imitation learning from incomplete demonstrations"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"

Connected through hyperedge HE-2 (induced by source paper "recent advances in imitation learning from observation"):
  with members:
    - "hybrid reinforcement learning with expert state sequences"
    - "sample efficient adversarial imitation learning from observation"
    - "imitation learning from video by leveraging proprioception"
    - "3d human pose machines with self supervised learning"
    - "ridm reinforced inverse dynamics modeling for learning from a single observed demonstration"
    - "internal model from observations for reward shaping"
    - "an algorithmic perspective on imitation learning"
    - "one shot learning of multi step tasks from observation via activity localization in auxiliary video"

Connected through hyperedge HE-3 (induced by source paper "provable representation learning for imitation learning via bi level optimization"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "online meta learning"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "batch policy learning under constraints"
    - "meta learning with implicit gradients"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "task embedded control networks for few shot imitation learning"

Connected through hyperedge HE-4 (induced by source paper "state alignment based imitation learning"):
  with members:
    - "wasserstein adversarial imitation learning"
    - "provably efficient imitation learning from observation alone"
    - "efficient exploration via state marginal matching"
    - "imitation learning as f divergence minimization"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "observe and look further achieving consistent performance on atari"

Connected through hyperedge HE-5 (induced by source paper "provably efficient imitation learning from observation alone"):
  with members:
    - "information theoretic considerations in batch reinforcement learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "model based rl in contextual decision processes pac bounds and exponential improvements over model free approaches"
    - "behavioral cloning from observation"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "agile autonomous driving using end to end deep imitation learning"

Connected through hyperedge HE-6 (induced by source paper "learning navigation subroutines from egocentric videos"):
  with members:
    - "pyrobot an open source robotics framework for research and benchmarking"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"
    - "visual memory for robust path following"
    - "on evaluation of embodied navigation agents"
    - "sfv reinforcement learning of physical skills from videos"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-7 (induced by source paper "leveraging human guidance for deep reinforcement learning tasks"):
  with members:
    - "imitation learning from video by leveraging proprioception"
    - "atari head atari human eye tracking and demonstration dataset"
    - "recent advances in imitation learning from observation"
    - "an algorithmic perspective on imitation learning"
    - "agil learning attention from human for visuomotor tasks"
    - "ordered preference elicitation strategies for supporting multi objective decision making"
    - "learning actionable representations from visual observations"
    - "generative adversarial imitation from observation"

Connected through hyperedge HE-8 (induced by source paper "smirl surprise minimizing rl in dynamic environments"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "self supervised exploration via disagreement"
    - "provably efficient maximum entropy exploration"
    - "exploration by random network distillation"
    - "playing hard exploration games by watching youtube"
    - "large scale study of curiosity driven learning"
    - "model based active exploration"
    - "generative adversarial imitation from observation"

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)