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: "planning with a receding horizon for manipulation in clutter using a learned value function"
  Abstract: Manipulation in clutter requires solving complex sequential decision making problems in an environment rich with physical interactions. The transfer of motion planning solutions from simulation to the real world, in open-loop, suffers from the inherent uncertainty in modelling real world physics. We propose interleaving planning and execution in real-time, in a closed-loop setting, using a Receding Horizon Planner (RHP) for pushing manipulation in clutter. In this context, we address the problem of finding a suitable value function based heuristic for efficient planning, and for estimating the cost-to-go from the horizon to the goal. We estimate such a value function first by using plans generated by an existing sampling-based planner. Then, we further optimize the value function through reinforcement learning. We evaluate our approach and compare it to state-of-the-art planning techniques for manipulation in clutter. We conduct experiments in simulation with artificially injected uncertainty on the physics parameters, as well as in real world tasks of manipulation in clutter. We show that this approach enables the robot to react to the uncertain dynamics of the real world effectively.

Connected through hyperedge HE-1 (induced by source paper "learning physics based manipulation in clutter combining image based generalization and look ahead planning"):
  with members:
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "deep q learning from demonstrations"
    - "thinking fast and slow with deep learning and tree search"

Connected through hyperedge HE-2 (induced by source paper "action guidance with mcts for deep reinforcement learning"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "wisemove a framework for safe deep reinforcement learning for autonomous driving"
    - "monte carlo tree search for simulation based strategy analysis"
    - "pommerman a multi agent playground"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "deep reinforcement learning a brief survey"
    - "deep reinforcement learning an overview"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-3 (induced by source paper "multi object rearrangement with monte carlo tree search a case study on planar nonprehensile sorting"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "learning manipulation states and actions for efficient non prehensile rearrangement planning"
    - "monte carlo tree search for efficient visually guided rearrangement planning"
    - "sample efficient learning of nonprehensile manipulation policies via physics based informed state distributions"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "high quality tabletop rearrangement with overhand grasps hardness results and fast methods"

Connected through hyperedge HE-4 (induced by source paper "non prehensile manipulation in clutter with human in the loop"):
  with members:
    - "efficient obstacle rearrangement for object manipulation tasks in cluttered environments"
    - "planning for target retrieval using a robotic manipulator in cluttered and occluded environments"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "online interactive user guidance for high dimensional constrained motion planning"

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.RO(Robotics)