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 "learning to manipulate deformable objects without demonstrations"):
  Abstract: In this paper we tackle the problem of deformable object manipulation through model-free visual reinforcement learning (RL). In order to circumvent the sample inefficiency of RL, we propose two key ideas that accelerate learning. First, we propose an iterative pick-place action space that encodes the conditional relationship between picking and placing on deformable objects. The explicit structural encoding enables faster learning under complex object dynamics. Second, instead of jointly learning both the pick and the place locations, we only explicitly learn the placing policy conditioned on random pick points. Then, by selecting the pick point that has Maximal Value under Placing (MVP), we obtain our picking policy. Using this learning framework, we obtain an order of magnitude faster learning compared to independent action-spaces on our suite of deformable object manipulation tasks. Finally, using domain randomization, we transfer our policies to a real PR2 robot for challenging cloth and rope manipulation.

Includes members:
  Member "deep imitation learning of sequential fabric smoothing policies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning predictive representations for deformable objects using contrastive estimation")
  Member "learning robotic manipulation through visual planning and acting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")
  Member "deep transfer learning of pick points on fabric for robot bed making":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning predictive representations for deformable objects using contrastive estimation")
  Member "rlpyt a research code base for deep reinforcement learning in pytorch":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "robot bed making deep transfer learning using depth sensing of deformable fabric":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "keto learning keypoint representations for tool manipulation")
  Member "deepmind control suite":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "model ensemble trust region policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ready policy one world building through active learning")
  Member "learning dexterous in hand manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "never give up learning directed exploration strategies")

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)