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: "fully convolutional grasp detection network with oriented anchor box"
  Abstract: In this paper, we present a real-time approach to predict multiple grasping poses for a parallel-plate robotic gripper using RGB images. A model with oriented anchor box mechanism is proposed and a new matching strategy is used during the training process. An end-to-end fully convolutional neural network is employed in our work. The network consists of two parts: the feature extractor and multi-grasp predictor. The feature extractor is a deep convolutional neural network. The multi-grasp predictor regresses grasp rectangles from predefined oriented rectangles, called oriented anchor boxes, and classifies the rectangles into graspable and ungraspable. On the standard Cornell Grasp Dataset, our model achieves an accuracy of 97.74% and 96.61% on image-wise split and object-wise split respectively, and outperforms the latest state-of-the-art approach by 1.74% on image-wise split and 0.51% on object-wise split.

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"
    - "roi based robotic grasp detection for object overlapping scenes"
    - "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"
    - "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 "egad an evolved grasping analysis dataset for diversity and reproducibility in robotic manipulation"):
  with members:
    - "pointnetgpd detecting grasp configurations from point sets"
    - "towards the targeted environment specific evolution of robot components"
    - "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-3 (induced by source paper "optimizing correlated graspability score and grasp regression for better grasp prediction"):
  with members:
    - "jacquard a large scale dataset for robotic grasp 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"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (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"
    - "roi based robotic grasp detection in object overlapping scenes using convolutional neural network"
    - "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-5 (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"
    - "roi based robotic grasp detection in object overlapping scenes using convolutional neural network"
    - "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-6 (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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "antipodal robotic grasping using generative residual convolutional neural network"):
  with members:
    - "learning probabilistic multi modal actor models for vision based robotic grasping"
    - "data efficient learning for sim to real robotic grasping using deep point cloud prediction networks"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "jacquard a large scale dataset for robotic grasp detection"
    - "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"
    - "depth image inpainting improving low rank matrix completion with low gradient regularization"

Connected through hyperedge HE-8 (induced by source paper "roi based robotic grasp detection for object overlapping scenes"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "visual manipulation relationship network"
    - "dealing with ambiguity in robotic grasping via multiple predictions"
    - "real world multi object multi grasp detection"
    - "jacquard a large scale dataset for robotic grasp detection"
    - "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"

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)