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 "learning navigation subroutines from egocentric videos"):
  Abstract: Planning at a higher level of abstraction instead of low level torques improves the sample efficiency in reinforcement learning, and computational efficiency in classical planning. We propose a method to learn such hierarchical abstractions, or subroutines from egocentric video data of experts performing tasks. We learn a self-supervised inverse model on small amounts of random interaction data to pseudo-label the expert egocentric videos with agent actions. Visuomotor subroutines are acquired from these pseudo-labeled videos by learning a latent intent-conditioned policy that predicts the inferred pseudo-actions from the corresponding image observations. We demonstrate our proposed approach in context of navigation, and show that we can successfully learn consistent and diverse visuomotor subroutines from passive egocentric videos. We demonstrate the utility of our acquired visuomotor subroutines by using them as is for exploration, and as sub-policies in a hierarchical RL framework for reaching point goals and semantic goals. We also demonstrate behavior of our subroutines in the real world, by deploying them on a real robotic platform. Project website: this https URL.

Includes members:
  Member "pyrobot an open source robotics framework for research and benchmarking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "audio visual embodied navigation")
  Member "divis domain invariant visual servoing for collision free goal reaching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning view and target invariant visual servoing for navigation")
  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 "deepmimic example guided deep reinforcement learning of physics based character skills":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "artificial intelligence for prosthetics challenge solutions")
  Member "playing hard exploration games by watching youtube":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "visual memory for robust path following":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning view and target invariant visual servoing for navigation")
  Member "on evaluation of embodied navigation agents":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an exploration of embodied visual exploration")
  Member "sfv reinforcement learning of physical skills from videos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "do as i do transferring human motion and appearance between monocular videos with spatial and temporal constraints")

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)