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 "one shot hierarchical imitation learning of compound visuomotor tasks"):
  Abstract: We consider the problem of learning multi-stage vision-based tasks on a real robot from a single video of a human performing the task, while leveraging demonstration data of subtasks with other objects. This problem presents a number of major challenges. Video demonstrations without teleoperation are easy for humans to provide, but do not provide any direct supervision. Learning policies from raw pixels enables full generality but calls for large function approximators with many parameters to be learned. Finally, compound tasks can require impractical amounts of demonstration data, when treated as a monolithic skill. To address these challenges, we propose a method that learns both how to learn primitive behaviors from video demonstrations and how to dynamically compose these behaviors to perform multi-stage tasks by "watching" a human demonstrator. Our results on a simulated Sawyer robot and real PR2 robot illustrate our method for learning a variety of order fulfillment and kitchen serving tasks with novel objects and raw pixel inputs.

Includes members:
  Member "neural task graphs generalizing to unseen tasks from a single video demonstration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to generalize across long horizon tasks from human demonstrations")
  Member "taco learning task decomposition via temporal alignment for control":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "active task inference guided deep inverse reinforcement learning")
  Member "multi modal imitation learning from unstructured demonstrations using generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "machine learning approaches for motor learning a short review")
  Member "translating videos to commands for robotic manipulation with deep recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding contexts inside robot and human manipulation tasks through a vision language model and ontology system in a video stream")
  Member "imitation from observation learning to imitate behaviors from raw video via context translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "visual task progress estimation with appearance invariant embeddings for robot control and planning")
  Member "neural task programming learning to generalize across hierarchical tasks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "good robot efficient reinforcement learning for multi step visual tasks with sim to real transfer")
  Member "vision based multi task manipulation for inexpensive robots using end to end learning from demonstration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "time contrastive networks self supervised learning from video":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")

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)