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: "deep reinforcement learning for high precision assembly tasks"
  Abstract: High precision assembly of mechanical parts requires accuracy exceeding the robot precision. Conventional part mating methods used in the current manufacturing requires tedious tuning of numerous parameters before deployment. We show how the robot can successfully perform a tight clearance peg-in-hole task through training a recurrent neural network with reinforcement learning. In addition to saving the manual effort, the proposed technique also shows robustness against position and angle errors for the peg-in-hole task. The neural network learns to take the optimal action by observing the robot sensors to estimate the system state. The advantages of our proposed method is validated experimentally on a 7-axis articulated robot arm.

Connected through hyperedge HE-1 (induced by source paper "balance between efficient and effective learning dense2sparse reward shaping for robot manipulation with environment uncertainty"):
  with members:
    - "contextual imagined goals for self supervised robotic learning"
    - "reward shaping via meta learning"
    - "dynamical distance learning for unsupervised and semi supervised skill discovery"
    - "motion perception in reinforcement learning with dynamic objects"
    - "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning with perturbed rewards"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-2 (induced by source paper "dynamic experience replay"):
  with members:
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "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"
    - "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 "sim2real transfer for reinforcement learning without dynamics randomization"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "contact skill imitation learning for robot independent assembly programming"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "soft actor critic algorithms and applications"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "progressive neural networks"

Connected through hyperedge HE-4 (induced by source paper "assembly robots with optimized control stiffness through reinforcement learning"):
  with members:
    - "fast skill learning for variable compliance robotic assembly"
    - "reinforcement learning on variable impedance controller for high precision 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"
    - "sim to real robot learning from pixels with progressive nets"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-5 (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"
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"

Connected through hyperedge HE-6 (induced by source paper "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"):
  with members:
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "quickly inserting pegs into uncertain holes using multi view images and deep network trained on synthetic data"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "a probabilistic framework for tracking uncertainties in robotic manipulation"
    - "learning robotic assembly from cad"
    - "learning dexterous in hand manipulation"
    - "deep learning scooping motion using bilateral teleoperations"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "experimental force torque dataset for robot learning of multi shape insertion"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"

Connected through hyperedge HE-8 (induced by source paper "reinforcement learning on variable impedance controller for high precision robotic assembly"):
  with members:
    - "residual reinforcement learning for robot control"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "learning from the hindsight plan episodic mpc improvement"
    - "guided policy search as approximate mirror descent"
    - "asynchronous methods for deep reinforcement learning"
    - "path integral guided policy search"
    - "one shot learning of manipulation skills with online dynamics adaptation and neural network priors"
    - "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)