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: "monte carlo tree search for efficient visually guided rearrangement planning"
  Abstract: In this paper, we address the problem of visually guided rearrangement planning with many movable objects, i.e., finding a sequence of actions to move a set of objects from an initial arrangement to a desired one, while relying directly on visual inputs coming from a camera. We introduce an efficient and scalable rearrangement planning method, addressing a fundamental limitation of most existing approaches that do not scale well with the number of objects. This increased efficiency allows us to use planning in a closed loop with visual workspace analysis to build a robust rearrangement framework that can recover from errors and external perturbations. The contributions of this work are threefold. First, we develop an AlphaGo-like strategy for rearrangement planning, improving the efficiency of Monte-Carlo Tree Search (MCTS) using a policy trained from rearrangement planning examples. We show empirically that the proposed approach scales well with the number of objects. Second, in order to demonstrate the efficiency of the planner on a real robot, we adopt a state-of-the-art calibration-free visual recognition system that outputs position of a single object and extend it to estimate the state of a workspace containing multiple objects. Third, we validate the complete pipeline with several experiments on a real UR-5 robotic arm solving rearrangement planning problems with multiple movable objects and only requiring few seconds of computation to compute the plan. We also show empirically that the robot can successfully recover from errors and perturbations in the workspace. Source code and pretrained models for our work are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "goal conditioned end to end visuomotor control for versatile skill primitives"):
  with members:
    - "hierarchical foresight self supervised learning of long horizon tasks via visual subgoal generation"
    - "object centric forward modeling for model predictive control"
    - "good robot efficient reinforcement learning for multi step visual tasks via reward shaping"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "unsupervised visuomotor control through distributional planning networks"
    - "universal planning networks"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"

Connected through hyperedge HE-2 (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"
    - "sample efficient learning of nonprehensile manipulation policies via physics based informed state distributions"
    - "planning with a receding horizon for manipulation in clutter using a learned value function"
    - "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-3 (induced by source paper "stylus a temporal logic optimal control synthesis algorithm for large scale multi robot systems"):
  with members:
    - "probabilistically safe corridors to guide sampling based motion planning"
    - "t a heuristic search based algorithm for motion planning with temporal goals"
    - "control of magnetic microrobot teams for temporal micromanipulation tasks"
    - "learning sampling distributions for robot motion planning"
    - "decentralized abstractions for multi agent systems under coupled constraints"
    - "sampling based temporal logic path planning"
    - "optimal multi robot path planning with ltl constraints guaranteeing correctness through synchronization"
    - "sampling based algorithms for optimal 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)