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: "pointnetgpd detecting grasp configurations from point sets"
  Abstract: In this paper, we propose an end-to-end grasp evaluation model to address the challenging problem of localizing robot grasp configurations directly from the point cloud. Compared to recent grasp evaluation metrics that are based on handcrafted depth features and a convolutional neural network (CNN), our proposed PointNetGPD is lightweight and can directly process the 3D point cloud that locates within the gripper for grasp evaluation. Taking the raw point cloud as input, our proposed grasp evaluation network can capture the complex geometric structure of the contact area between the gripper and the object even if the point cloud is very sparse. To further improve our proposed model, we generate a larger-scale grasp dataset with 350k real point cloud and grasps with the YCB object set for training. The performance of the proposed model is quantitatively measured both in simulation and on robotic hardware. Experiments on object grasping and clutter removal show that our proposed model generalizes well to novel objects and outperforms state-of-the-art methods. Code and video are available at \href{this https URL}{this https URL}

Connected through hyperedge HE-1 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping"):
  with members:
    - "directional semantic grasping of real world objects from simulation to reality"
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "metagrasp data efficient grasping by affordance interpreter network"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "6 dof graspnet variational grasp generation for object manipulation"

Connected through hyperedge HE-2 (induced by source paper "regnet region based grasp network for single shot grasp detection in point clouds"):
  with members:
    - "object detection in 20 years a survey"
    - "relation shape convolutional neural network for point cloud analysis"
    - "roi based robotic grasp detection for object overlapping scenes"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "a real time robotic grasp approach with oriented anchor box"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "so net self organizing network for point cloud analysis"
    - "jacquard a large scale dataset for robotic grasp detection"

Connected through hyperedge HE-3 (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"
    - "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"
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"

Connected through hyperedge HE-4 (induced by source paper "egad an evolved grasping analysis dataset for diversity and reproducibility in robotic manipulation"):
  with members:
    - "towards the targeted environment specific evolution of robot components"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "diversity is all you need learning skills without a reward function"
    - "densely supervised grasp detector dsgd"
    - "multi view picking next best view reaching for improved grasping in clutter"
    - "jacquard a large scale dataset for robotic grasp detection"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "grasp pose detection in point clouds"

Connected through hyperedge HE-5 (induced by source paper "6 dof graspnet variational grasp generation for object manipulation"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "generating grasp poses for a high dof gripper using neural networks"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "splatnet sparse lattice networks for point cloud processing"
    - "dynamic graph cnn for learning on point clouds"
    - "learning 6 dof grasping interaction with deep geometry aware 3d representations"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-6 (induced by source paper "graspnet a large scale clustered and densely annotated datase for object grasping"):
  with members:
    - "self supervised 6d object pose estimation for robot manipulation"
    - "s4g amodal single view single shot se 3 grasp detection in cluttered scenes"
    - "roi based robotic grasp detection for object overlapping scenes"
    - "6 dof graspnet variational grasp generation for object 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"
    - "estimating 6d pose from localizing designated surface keypoints"
    - "roi based robotic grasp detection in object overlapping scenes using convolutional neural network"

Connected through hyperedge HE-7 (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"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "grasping unknown objects in clutter by superquadric representation"

Connected through hyperedge HE-8 (induced by source paper "s4g amodal single view single shot se 3 grasp detection in cluttered scenes"):
  with members:
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "learning 6 dof grasping and pick place using attention focus"
    - "grasp2vec learning object representations from self supervised grasping"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "on the continuity of rotation representations in neural networks"
    - "grasp pose detection in point clouds"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

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)