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 "recent advances in imitation learning from observation"):
  Abstract: Imitation learning is the process by which one agent tries to learn how to perform a certain task using information generated by another, often more-expert agent performing that same task. Conventionally, the imitator has access to both state and action information generated by an expert performing the task (e.g., the expert may provide a kinesthetic demonstration of object placement using a robotic arm). However, requiring the action information prevents imitation learning from a large number of existing valuable learning resources such as online videos of humans performing tasks. To overcome this issue, the specific problem of imitation from observation (IfO) has recently garnered a great deal of attention, in which the imitator only has access to the state information (e.g., video frames) generated by the expert. In this paper, we provide a literature review of methods developed for IfO, and then point out some open research problems and potential future work.

Includes members:
  Member "hybrid reinforcement learning with expert state sequences":
    also connected through other hyperedges:
      (none)
  Member "sample efficient adversarial imitation learning from observation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ridm reinforced inverse dynamics modeling for learning from a single observed demonstration")
  Member "imitation learning from video by leveraging proprioception":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning predictive models from observation and interaction")
  Member "3d human pose machines with self supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multiview consistent semi supervised learning for 3d human pose estimation")
  Member "ridm reinforced inverse dynamics modeling for learning from a single observed demonstration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "imitation learning from video by leveraging proprioception")
  Member "imitating latent policies from observation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning predictive models from observation and interaction")
  Member "internal model from observations for reward shaping":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforced imitation in heterogeneous action space")
  Member "an algorithmic perspective on imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations")

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.RO(Robotics)