human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "recent advances in imitation learning from observation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "domain adversarial and conditional state space model for imitation learning")
  Member "ridm reinforced inverse dynamics modeling for learning from a single observed demonstration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recent advances in imitation learning from observation")
  Member "imitating latent policies from observation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning predictive models from observation and interaction")
  Member "an algorithmic perspective on imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations")
  Member "one shot learning of multi step tasks from observation via activity localization in auxiliary video":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "recent advances in imitation learning from observation")
  Member "adversarial imitation via variational inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficiently guiding imitation learning algorithms with human gaze")
  Member "generative adversarial imitation from observation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving")
  Member "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "universal value density estimation for imitation learning and goal conditioned reinforcement learning")

Question: Please tell me which class the source paper 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)