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: "using geometry to detect grasps in 3d point clouds"
  Abstract: This paper proposes a new approach to detecting grasp points on novel objects presented in clutter. The input to our algorithm is a point cloud and the geometric parameters of the robot hand. The output is a set of hand configurations that are expected to be good grasps. Our key idea is to use knowledge of the geometry of a good grasp to improve detection. First, we use a geometrically necessary condition to sample a large set of high quality grasp hypotheses. We were surprised to find that using simple geometric conditions for detection can result in a relatively high grasp success rate. Second, we use the notion of an antipodal grasp (a standard characterization of a good two fingered grasp) to help us classify these grasp hypotheses. In particular, we generate a large automatically labeled training set that gives us high classification accuracy. Overall, our method achieves an average grasp success rate of 88% when grasping novels objects presented in isolation and an average success rate of 73% when grasping novel objects presented in dense clutter. This system is available as a ROS package at this http URL

Connected through hyperedge HE-1 (induced by source paper "domain independent unsupervised learning to grasp the novel objects"):
  with members:
    - "learning a visuomotor controller for real world robotic grasping using easily simulated depth images"
    - "robotic grasp detection using deep convolutional neural networks"
    - "high precision grasp pose detection in dense clutter"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "real time grasp detection using convolutional neural networks"
    - "data driven grasp synthesis a survey"

Connected through hyperedge HE-2 (induced by source paper "replab a reproducible low cost arm benchmark platform for robotic learning"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning dexterous in hand manipulation"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "visual reinforcement learning with imagined goals"
    - "addressing function approximation error in actor critic methods"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "deep reinforcement learning that matters"
    - "the feeling of success does touch sensing help predict grasp outcomes"

Connected through hyperedge HE-3 (induced by source paper "generative grasp synthesis from demonstration using parametric mixtures"):
  with members:
    - "high precision grasp pose detection in dense clutter"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"

Connected through hyperedge HE-4 (induced by source paper "pointnetgpd detecting grasp configurations from point sets"):
  with members:
    - "so net self organizing network for point cloud analysis"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "grasp pose detection in point clouds"
    - "octnet learning deep 3d representations at high resolutions"
    - "shape completion enabled robotic grasping"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "multi view 3d object detection network for autonomous driving"
    - "deep sliding shapes for amodal 3d object detection in rgb d images"

Connected through hyperedge HE-5 (induced by source paper "learning based industrial bin picking trained with approximate physics simulator"):
  with members:
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "robotic pick and place of novel objects in clutter with multi affordance grasping and cross domain image matching"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "initial experiments on learning based randomized bin picking allowing finger contact with neighboring objects"
    - "imagenet large scale visual recognition challenge"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-6 (induced by source paper "experiments on learning based industrial bin picking with iterative visual recognition"):
  with members:
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "iterative visual recognition for learning based randomized bin picking"
    - "initial experiments on learning based randomized bin picking allowing finger contact with neighboring objects"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-7 (induced by source paper "a novel geometry based algorithm for robust grasping in extreme clutter environment"):
  with members:
    - "high precision grasp pose detection in dense clutter"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "deep learning for detecting robotic grasps"
    - "localizing grasp affordances in 3 d points clouds using taubin quadric fitting"

Connected through hyperedge HE-8 (induced by source paper "optimized edge based grasping method for a cluttered environment"):
  with members:
    - "high precision grasp pose detection in dense clutter"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "deep learning for detecting robotic grasps"
    - "localizing grasp affordances in 3 d points clouds using taubin quadric fitting"

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)