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 "reinforcement learning on variable impedance controller for high precision robotic assembly"):
  Abstract: Precise robotic manipulation skills are desirable in many industrial settings, reinforcement learning (RL) methods hold the promise of acquiring these skills autonomously. In this paper, we explicitly consider incorporating operational space force/torque information into reinforcement learning; this is motivated by humans heuristically mapping perceived forces to control actions, which results in completing high-precision tasks in a fairly easy manner. Our approach combines RL with force/torque information by incorporating a proper operational space force controller; where we also exploit different ablations on processing this information. Moreover, we propose a neural network architecture that generalizes to reasonable variations of the environment. We evaluate our method on the open-source Siemens Robot Learning Challenge, which requires precise and delicate force-controlled behavior to assemble a tight-fit gear wheel set.

Includes members:
  Member "residual reinforcement learning for robot control":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning contact rich manipulation tasks with rigid position controlled robots learning to force control")
  Member "deep reinforcement learning for high precision assembly tasks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "balance between efficient and effective learning dense2sparse reward shaping for robot manipulation with environment uncertainty")
  Member "combining model based and model free updates for trajectory centric reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multimodal trajectory optimization for motion planning")
  Member "learning from the hindsight plan episodic mpc improvement":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "information theoretic model predictive q learning")
  Member "guided policy search as approximate mirror descent":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic experience replay")
  Member "asynchronous methods for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "path integral guided policy search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "local policy optimization for trajectory centric reinforcement learning")
  Member "one shot learning of manipulation skills with online dynamics adaptation and neural network priors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hyper meta reinforcement learning with sparse reward")

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)