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 "compare contact model based control and contact model free learning a survey of robotic peg in hole assembly strategies"):
  Abstract: In this paper, we present an overview of robotic peg-in-hole assembly and analyze two main strategies: contact model-based and contact model-free strategies. More specifically, we first introduce the contact model control approaches, including contact state recognition and compliant control two steps. Additionally, we focus on a comprehensive analysis of the whole robotic assembly system. Second, without the contact state recognition process, we decompose the contact model-free learning algorithms into two main subfields: learning from demonstrations and learning from environments (mainly based on reinforcement learning). For each subfield, we survey the landmark studies and ongoing research to compare the different categories. We hope to strengthen the relation between these two research communities by revealing the underlying links. Ultimately, the remaining challenges and open questions in the field of robotic peg-in-hole assembly community is discussed. The promising directions and potential future work are also considered.

Includes members:
  Member "temporal difference models model free deep rl for model based control":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rewriting history with inverse rl hindsight inference for policy improvement")
  Member "learning robotic assembly from cad":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "a learning framework for high precision industrial assembly":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic experience replay")
  Member "deep reinforcement learning for high precision assembly tasks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "balance between efficient and effective learning dense2sparse reward shaping for robot manipulation with environment uncertainty")
  Member "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "benchmarking deep reinforcement learning for continuous control":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "learning contact rich manipulation skills with guided policy search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "let s push things forward a survey on robot pushing")

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)