human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras"):
  with members:
    - "pixel attentive policy gradient for multi fingered grasping in cluttered scenes"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "making sense of vision and touch learning multimodal representations for contact rich tasks"
    - "grip generative robust inference and perception for semantic robot manipulation in adversarial environments"
    - "learning pose estimation for high precision robotic assembly using simulated depth images"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning robotic assembly from cad"

Connected through hyperedge HE-2 (induced by source paper "dynamic experience replay"):
  with members:
    - "distributed prioritized experience replay"
    - "distributed distributional deterministic policy gradients"
    - "a learning framework for high precision industrial assembly"
    - "distributed deep reinforcement learning learn how to play atari games in 21 minutes"
    - "hindsight experience replay"
    - "deep reinforcement learning for high precision assembly tasks"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "rllib abstractions for distributed reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "assembly robots with optimized control stiffness through reinforcement learning"):
  with members:
    - "fast skill learning for variable compliance robotic assembly"
    - "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"
    - "a practical approach to insertion with variable socket position using deep reinforcement learning"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "progressive neural architecture search"
    - "deep reinforcement learning for high precision assembly tasks"
    - "sim to real robot learning from pixels with progressive nets"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-4 (induced by source paper "robotic assembly across multiple contact stiffnesses with robust force controllers"):
  with members:
    - "compare contact model based control and contact model free learning a survey of robotic peg in hole assembly strategies"
    - "convex controller synthesis for robot contact"
    - "convex controller synthesis for contact"
    - "deep reinforcement learning for high precision assembly tasks"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "temporal difference models model free deep rl for model based control"
    - "residual policy learning"
    - "learning robotic assembly from cad"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "visual reinforcement learning with imagined goals"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "residual reinforcement learning for robot control"

Connected through hyperedge HE-6 (induced by source paper "composing diverse policies for temporally extended tasks"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "using causal analysis to learn specifications from task demonstrations"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "deepmdp learning continuous latent space models for representation learning"
    - "from explanation to synthesis compositional program induction for learning from demonstration"
    - "learning safe policies with expert guidance"
    - "learning robotic assembly from cad"

Connected through hyperedge HE-7 (induced by source paper "learning to scaffold the development of robotic manipulation skills"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning robotic assembly from cad"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "deep reinforcement learning for high precision assembly tasks"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "bandits and experts in metric spaces"
    - "efficient optimal learning for contextual bandits"

Connected through hyperedge HE-8 (induced by source paper "learning variable impedance control for contact sensitive tasks"):
  with members:
    - "variable impedance control in end effector space an action space for reinforcement learning in contact rich tasks"
    - "learning a structured neural network policy for a hopping task"
    - "learning locomotion skills using deeprl does the choice of action space matter"
    - "momentum control with hierarchical inverse dynamics on a torque controlled humanoid"
    - "continuous control with deep reinforcement learning"

Question: Please tell me which class the center node 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)