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 "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"):
  Abstract: Connector insertion and many other tasks commonly found in modern manufacturing settings involve complex contact dynamics and friction. Since it is difficult to capture related physical effects with first-order modeling, traditional control methods often result in brittle and inaccurate controllers, which have to be manually tuned. Reinforcement learning (RL) methods have been demonstrated to be capable of learning controllers in such environments from autonomous interaction with the environment, but running RL algorithms in the real world poses sample efficiency and safety challenges. Moreover, in practical real-world settings we cannot assume access to perfect state information or dense reward signals. In this paper, we consider a variety of difficult industrial insertion tasks with visual inputs and different natural reward specifications, namely sparse rewards and goal images. We show that methods that combine RL with prior information, such as classical controllers or demonstrations, can solve these tasks from a reasonable amount of real-world interaction.

Includes members:
  Member "reinforcement learning on variable impedance controller for high precision robotic assembly":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "qt opt scalable deep reinforcement learning for vision based robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "temporal difference models model free deep rl for model based control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rewriting history with inverse rl hindsight inference for policy improvement")
  Member "residual policy learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "brpo batch residual policy optimization")
  Member "learning robotic assembly from cad":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "data efficient deep reinforcement learning for dexterous manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "visual reinforcement learning with imagined goals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "balance between efficient and effective learning dense2sparse reward shaping for robot manipulation with environment uncertainty")
  Member "dexterous manipulation with deep reinforcement learning efficient general and low cost":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "in hand object pose tracking via contact feedback and gpu accelerated robotic simulation")

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)