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: "real time online re planning for grasping under clutter and uncertainty"
  Abstract: We consider the problem of grasping in clutter. While there have been motion planners developed to address this problem in recent years, these planners are mostly tailored for open-loop execution. Open-loop execution in this domain, however, is likely to fail, since it is not possible to model the dynamics of the multi-body multi-contact physical system with enough accuracy, neither is it reasonable to expect robots to know the exact physical properties of objects, such as frictional, inertial, and geometrical. Therefore, we propose an online re-planning approach for grasping through clutter. The main challenge is the long planning times this domain requires, which makes fast re-planning and fluent execution difficult to realize. In order to address this, we propose an easily parallelizable stochastic trajectory optimization based algorithm that generates a sequence of optimal controls. We show that by running this optimizer only for a small number of iterations, it is possible to perform real time re-planning cycles to achieve reactive manipulation under clutter and uncertainty.

Connected through hyperedge HE-1 (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"
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "learning sampling distributions for robot motion planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (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"
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "learning sampling distributions for robot motion planning"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"

Connected through hyperedge HE-3 (induced by source paper "combining coarse and fine physics for manipulation using parallel in time integration"):
  with members:
    - "efficient computation of higher order variational integrators in robotic simulation and trajectory optimization"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "pushing fast and slow task adaptive planning for non prehensile manipulation under uncertainty"
    - "time integrating articulated body dynamics using position based collocation method"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "combining learned and analytical models for predicting action effects"

Connected through hyperedge HE-4 (induced by source paper "6 dof grasping for target driven object manipulation in clutter"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "mechanical search multi step retrieval of a target object occluded by clutter"
    - "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "grasping unknown objects in clutter by superquadric representation"

Connected through hyperedge HE-5 (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"
    - "planning with a receding horizon for manipulation in clutter using a learned value function"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "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-6 (induced by source paper "parareal with a learned coarse model for robotic manipulation"):
  with members:
    - "combining coarse and fine physics for manipulation using parallel in time integration"
    - "efficient computation of higher order variational integrators in robotic simulation and trajectory optimization"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "pushing fast and slow task adaptive planning for non prehensile manipulation under uncertainty"
    - "time integrating articulated body dynamics using position based collocation method"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "parallelizing over artificial neural network training runs with multigrid"
    - "combining learned and analytical models for predicting action effects"

Connected through hyperedge HE-7 (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"
    - "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-8 (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"
    - "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)