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: "a learning framework for high precision industrial assembly"
  Abstract: Automatic assembly has broad applications in industries. Traditional assembly tasks utilize predefined trajectories or tuned force control parameters, which make the automatic assembly time-consuming, difficult to generalize, and not robust to uncertainties. In this paper, we propose a learning framework for high precision industrial assembly. The framework combines both the supervised learning and the reinforcement learning. The supervised learning utilizes trajectory optimization to provide the initial guidance to the policy, while the reinforcement learning utilizes actor-critic algorithm to establish the evaluation system even the supervisor is not accurate. The proposed learning framework is more efficient compared with the reinforcement learning and achieves better stability performance than the supervised learning. The effectiveness of the method is verified by both the simulation and experiment.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "efficient robotic task generalization using deep model fusion reinforcement learning"):
  with members:
    - "hierarchical reinforcement learning framework towards multi agent navigation"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "a survey on policy search algorithms for learning robot controllers in a handful of trials"
    - "deep reinforcement learning that matters"
    - "hindsight experience replay"
    - "proximal policy optimization algorithms"
    - "generalizing skills with semi supervised reinforcement learning"
    - "prioritized experience replay"

Connected through hyperedge HE-3 (induced by source paper "compare contact model based control and contact model free learning a survey of robotic peg in hole assembly strategies"):
  with members:
    - "temporal difference models model free deep rl for model based control"
    - "learning robotic assembly from cad"
    - "deep reinforcement learning for high precision assembly tasks"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "benchmarking deep reinforcement learning for continuous control"
    - "learning contact rich manipulation skills with guided policy search"

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.AI(Artificial Intelligence)