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 in object overlapping scenes using convolutional neural network"
  Abstract: Grasp detection is an essential skill for widespread use of robots. Recent works demonstrate the advanced performance of Convolutional Neural Network (CNN) on robotic grasp detection. However, a significant shortcoming of existing grasp detection algorithms is that they all ignore the affiliation between grasps and targets. In this paper, we propose a robotic grasp detection algorithm based on Region of Interest (RoI) to simultaneously detect targets and their grasps in object overlapping scenes. Our proposed algorithm uses Regions of Interest (RoIs) to detect grasps while doing classification and location regression of targets. To train the network, we contribute a much bigger multi-object grasp dataset than Cornell Grasp Dataset, which is based on Visual Manipulation Relationship Dataset. Experimental results demonstrate that our algorithm achieves 24.9% miss rate at 1FPPI and 68.2% mAP with grasp on our dataset. Robotic experiments demonstrate that our proposed algorithm can help robots grasp specified target in multi-object scenes at 84% success rate.

Connected through hyperedge HE-1 (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"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "estimating 6d pose from localizing designated surface keypoints"

Connected through hyperedge HE-2 (induced by source paper "gq stn optimizing one shot grasp detection based on robustness classifier"):
  with members:
    - "dealing with ambiguity in robotic grasping via multiple predictions"
    - "rotation ensemble module for detecting rotation invariant features"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "classification based grasp detection using spatial transformer network"
    - "learning object localization and 6d pose estimation from simulation and weakly labeled real images"
    - "jacquard a large scale dataset for robotic grasp detection"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "robotic grasp detection using deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "real time highly accurate robotic grasp detection using fully convolutional neural network with rotation ensemble module"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "polar transformer networks"
    - "rotation equivariant vector field networks"
    - "deformable convolutional networks"
    - "robotic grasp detection using deep convolutional neural networks"
    - "yolo9000 better faster stronger"
    - "inverse compositional spatial transformer networks"

Connected through hyperedge HE-4 (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:
    - "roi based robotic grasp detection for object overlapping scenes"
    - "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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "efficient fully convolution neural network for generating pixel wise robotic grasps with high resolution images"):
  with members:
    - "real time highly accurate robotic grasp detection using fully convolutional neural networks with high resolution images"
    - "classification based grasp detection using spatial transformer network"
    - "deep grasp detection and localization of grasps with deep neural networks"
    - "robotic grasp detection using deep convolutional neural networks"
    - "real time grasp detection using convolutional neural networks"
    - "fully convolutional networks for semantic segmentation"
    - "deep learning for detecting robotic grasps"

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)