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: "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.

Connected through hyperedge HE-1 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "investigating the importance of shape features color constancy color spaces and similarity measures in open ended 3d object recognition"
    - "accelerating reinforcement learning for reaching using continuous curriculum learning"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "learning better generative models for dexterous single view grasping of novel objects"
    - "the sound of motions"
    - "co separating sounds of visual objects"
    - "cleargrasp 3d shape estimation of transparent objects for manipulation"

Connected through hyperedge HE-2 (induced by source paper "good robot efficient reinforcement learning for multi step visual tasks with sim to real transfer"):
  with members:
    - "form2fit learning shape priors for generalizable assembly from disassembly"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "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"
    - "playing hard exploration games by watching youtube"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "human grasp classification for reactive human to robot handovers"):
  with members:
    - "transferable task execution from pixels through deep planning domain learning"
    - "3d hand shape and pose estimation from a single rgb image"
    - "freihand a dataset for markerless capture of hand pose and shape from single rgb images"
    - "learning joint reconstruction of hands and manipulated objects"
    - "representing robot task plans as robust logical dynamical systems"
    - "honnotate a method for 3d annotation of hand and objects poses"
    - "online replanning in belief space for partially observable task and motion problems"
    - "dexpilot vision based teleoperation of dexterous robotic hand arm system"

Connected through hyperedge HE-4 (induced by source paper "gomp grasp optimized motion planning for bin picking"):
  with members:
    - "generating grasp poses for a high dof gripper using neural networks"
    - "manipulation trajectory optimization with online grasp synthesis and selection"
    - "a billion ways to grasp an evaluation of grasp sampling schemes on a dense physics based grasp data set"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "grasp pose detection in point clouds"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"

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)