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: "imitation learning from video by leveraging proprioception"
  Abstract: Classically, imitation learning algorithms have been developed for idealized situations, e.g., the demonstrations are often required to be collected in the exact same environment and usually include the demonstrator's actions. Recently, however, the research community has begun to address some of these shortcomings by offering algorithmic solutions that enable imitation learning from observation (IfO), e.g., learning to perform a task from visual demonstrations that may be in a different environment and do not include actions. Motivated by the fact that agents often also have access to their own internal states (i.e., proprioception), we propose and study an IfO algorithm that leverages this information in the policy learning process. The proposed architecture learns policies over proprioceptive state representations and compares the resulting trajectories visually to the demonstration data. We experimentally test the proposed technique on several MuJoCo domains and show that it outperforms other imitation from observation algorithms by a large margin.

Connected through hyperedge HE-1 (induced by source paper "learning predictive models from observation and interaction"):
  with members:
    - "experience embedded visual foresight"
    - "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"
    - "when to trust your model model based policy optimization"

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"
    - "3d human pose machines with self supervised learning"
    - "ridm reinforced inverse dynamics modeling for learning from a single observed demonstration"
    - "imitating latent policies from observation"
    - "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 "leveraging human guidance for deep reinforcement learning tasks"):
  with members:
    - "atari head atari human eye tracking and demonstration dataset"
    - "recent advances in imitation learning from observation"
    - "imitating latent policies 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-4 (induced by source paper "ridm reinforced inverse dynamics modeling for learning from a single observed demonstration"):
  with members:
    - "sample efficient adversarial imitation learning from observation"
    - "recent advances in imitation learning from observation"
    - "learning actionable representations from visual observations"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "generative adversarial imitation from observation"
    - "behavioral cloning from observation"
    - "deep reinforcement learning that matters"
    - "combining self supervised learning and imitation for vision based rope manipulation"

Connected through hyperedge HE-5 (induced by source paper "imitation learning from observations by minimizing inverse dynamics disagreement"):
  with members:
    - "maximum entropy generators for energy based models"
    - "learning dexterous in hand manipulation"
    - "generative adversarial imitation from observation"
    - "learning to walk via deep reinforcement learning"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "sample efficient adversarial imitation learning from observation"):
  with members:
    - "recent advances in imitation learning from observation"
    - "generative adversarial imitation from observation"
    - "multi agent generative adversarial imitation learning"
    - "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"

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)