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 "6 dof grasping for target driven object manipulation in clutter"):
  Abstract: Grasping in cluttered environments is a fundamental but challenging robotic skill. It requires both reasoning about unseen object parts and potential collisions with the manipulator. Most existing data-driven approaches avoid this problem by limiting themselves to top-down planar grasps which is insufficient for many real-world scenarios and greatly limits possible grasps. We present a method that plans 6-DOF grasps for any desired object in a cluttered scene from partial point cloud observations. Our method achieves a grasp success of 80.3%, outperforming baseline approaches by 17.6% and clearing 9 cluttered table scenes (which contain 23 unknown objects and 51 picks in total) on a real robotic platform. By using our learned collision checking module, we can even reason about effective grasp sequences to retrieve objects that are not immediately accessible. Supplementary video can be found at this https URL.

Includes members:
  Member "planning multi fingered grasps as probabilistic inference in a learned deep network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi fingered grasp planning via inference in deep neural networks")
  Member "mechanical search multi step retrieval of a target object occluded by clutter":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "toward sim to real directional semantic grasping")
  Member "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "good robot efficient reinforcement learning for multi step visual tasks with sim to real transfer")
  Member "6 dof graspnet variational grasp generation for object manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "qt opt scalable deep reinforcement learning for vision based robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "pointnetgpd detecting grasp configurations from point sets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "real time online re planning for grasping under clutter and uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "human like planning for reaching in cluttered environments")
  Member "robot learning in homes improving generalization and reducing dataset bias":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning predictive representations for deformable objects using contrastive estimation")

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)