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 physics based manipulation in clutter combining image based generalization and look ahead planning"):
  Abstract: Physics-based manipulation in clutter involves complex interaction between multiple objects. In this paper, we consider the problem of learning, from interaction in a physics simulator, manipulation skills to solve this multi-step sequential decision making problem in the real world. Our approach has two key properties: (i) the ability to generalize (over the shape and number of objects in the scene) using an abstract image-based representation that enables a neural network to learn useful features; and (ii) the ability to perform look-ahead planning using a physics simulator, which is essential for such multi-step problems. We show, in sets of simulated and real-world experiments (video available on this https URL), that by learning to evaluate actions in an abstract image-based representation of the real world, the robot can generalize and adapt to the object shapes in challenging real-world environments.

Includes members:
  Member "learning dexterous in hand manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "never give up learning directed exploration strategies")
  Member "sim to real transfer of robotic control with dynamics randomization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "learning synergies between pushing and grasping with self supervised deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "planning with a receding horizon for manipulation in clutter using a learned value function":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "action guidance with mcts for deep reinforcement learning")
  Member "plan online learn offline efficient learning and exploration via model based control":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural lyapunov model predictive control")
  Member "real time online re planning for grasping under clutter and uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "human like planning for reaching in cluttered environments")
  Member "domain randomization for transferring deep neural networks from simulation to the real world":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "deep q learning from demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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)