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 "efficient reinforcement learning control for continuum robots based on inexplicit prior knowledge"):
  Abstract: Compared to rigid robots that are often studied in reinforcement learning, the physical characteristics of some sophisticated robots such as software or continuum are more complicated. Moreover, recent reinforcement learning methods are data-inefficient and can not be directly deployed to the robot without simulation. In this paper, we propose an efficient reinforcement learning method based on inexplicit prior knowledge in response to such problems. The method is firstly corroborated by simulation and employed directly in the real world. By using our method, we can achieve visual active tracking and distance maintenance of a tendon-driven robot which will be critical in minimally-invasive procedures.

Includes members:
  Member "when to trust your model model based policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "deep reinforcement learning boosted by external knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "accounting for the neglected dimensions of ai progress")
  Member "qt opt scalable deep reinforcement learning for vision based robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "model ensemble trust region policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ready policy one world building through active learning")
  Member "sim to real transfer of robotic control with dynamics randomization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "model based value estimation for efficient model free reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "value prediction network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")

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)