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: "randomized physics based motion planning for grasping in cluttered and uncertain environments"
  Abstract: Planning motions to grasp an object in cluttered and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exist and objects obstructing the way are required to be carefully grasped and moved out. This paper takes a different approach and proposes to address this problem by using a randomized physics-based motion planner that permits robot-object and object-object interactions. The main idea is to avoid an explicit high-level reasoning of the task by providing the motion planner with a physics engine to evaluate possible complex multi-body dynamical interactions. The approach is able to solve the problem in complex scenarios, also considering uncertainty in the objects pose and in the contact dynamics. The work enhances the state validity checker, the control sampler and the tree exploration strategy of a kinodynamic motion planner called KPIECE. The enhanced algorithm, called p-KPIECE, has been validated in simulation and with real experiments. The results have been compared with an ontological physics-based motion planner and with task and motion planning approaches, resulting in a significant improvement in terms of planning time, success rate and quality of the solution path.

Connected through hyperedge HE-1 (induced by source paper "learning pregrasp manipulation of objects from ungraspable poses"):
  with members:
    - "robust execution of contact rich motion plans by hybrid force velocity control"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning dexterous in hand manipulation"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"

Connected through hyperedge HE-2 (induced by source paper "human like planning for reaching in cluttered environments"):
  with members:
    - "combining coarse and fine physics for manipulation using parallel in time integration"
    - "deepmind control suite"
    - "pushing fast and slow task adaptive planning for non prehensile manipulation under uncertainty"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "learning sampling distributions for robot motion planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "introducing a human like planner for reaching in cluttered environments"):
  with members:
    - "deepmind control suite"
    - "pushing fast and slow task adaptive planning for non prehensile manipulation under uncertainty"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "learning sampling distributions for robot motion planning"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"

Connected through hyperedge HE-4 (induced by source paper "planning with selective physics based simulation for manipulation among movable objects"):
  with members:
    - "pushpush and push 1 are np hard in 2d"

Connected through hyperedge HE-5 (induced by source paper "mechanical search multi step retrieval of a target object occluded by clutter"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "one shot segmentation in clutter"
    - "segmenting unknown 3d objects from real depth images using mask r cnn trained on synthetic data"
    - "visual representations for semantic target driven navigation"
    - "interactive perception leveraging action in perception and perception in action"
    - "neural task programming learning to generalize across hierarchical tasks"

Connected through hyperedge HE-6 (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"
    - "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"
    - "high quality tabletop rearrangement with overhand grasps hardness results and fast methods"

Connected through hyperedge HE-7 (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"
    - "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"
    - "online interactive user guidance for high dimensional constrained motion planning"

Connected through hyperedge HE-8 (induced by source paper "planning for target retrieval using a robotic manipulator in cluttered and occluded environments"):
  with members:
    - "efficient obstacle rearrangement for object manipulation tasks in cluttered environments"
    - "faster r cnn towards real time object detection with region proposal networks"

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)