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: "roi based robotic grasp detection for object overlapping scenes"
  Abstract: Grasp detection with consideration of the affiliations between grasps and their owner in object overlapping scenes is a necessary and challenging task for the practical use of the robotic grasping approach. In this paper, a robotic grasp detection algorithm named ROI-GD is proposed to provide a feasible solution to this problem based on Region of Interest (ROI), which is the region proposal for objects. ROI-GD uses features from ROIs to detect grasps instead of the whole scene. It has two stages: the first stage is to provide ROIs in the input image and the second-stage is the grasp detector based on ROI features. We also contribute a multi-object grasp dataset, which is much larger than Cornell Grasp Dataset, by labeling Visual Manipulation Relationship Dataset. Experimental results demonstrate that ROI-GD performs much better in object overlapping scenes and at the meantime, remains comparable with state-of-the-art grasp detection algorithms on Cornell Grasp Dataset and Jacquard Dataset. Robotic experiments demonstrate that ROI-GD can help robots grasp the target in single-object and multi-object scenes with the overall success rates of 92.5% and 83.8% respectively.

Connected through hyperedge HE-1 (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"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "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-2 (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"
    - "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"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "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-3 (induced by source paper "a single multi task deep neural network with post processing for object detection with reasoning and robotic grasp detection"):
  with members:
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "yolov3 an incremental improvement"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "roi based robotic grasp detection in object overlapping scenes using convolutional neural network"
    - "focal loss for dense object detection"
    - "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning"
    - "robotic grasp detection using deep convolutional neural networks"
    - "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)