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: "real time highly accurate robotic grasp detection using fully convolutional neural networks with high resolution images"
  Abstract: Robotic grasp detection for novel objects is a challenging task, but for the last few years, deep learning based approaches have achieved remarkable performance improvements, up to 96.1% accuracy, with RGB-D data. In this paper, we propose fully convolutional neural network (FCNN) based methods for robotic grasp detection. Our methods also achieved state-of-the-art detection accuracy (up to 96.6%) with state-of- the-art real-time computation time for high-resolution images (6-20ms per 360x360 image) on Cornell dataset. Due to FCNN, our proposed method can be applied to images with any size for detecting multigrasps on multiobjects. Proposed methods were evaluated using 4-axis robot arm with small parallel gripper and RGB-D camera for grasping challenging small, novel objects. With accurate vision-robot coordinate calibration through our proposed learning-based, fully automatic approach, our proposed method yielded 90% success rate.

Connected through hyperedge HE-1 (induced by source paper "vision based robotic grasping from object localization pose estimation grasp detection to motion planning a review"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pixor real time 3d object detection from point clouds"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "bop benchmark for 6d object pose estimation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"

Connected through hyperedge HE-2 (induced by source paper "camera to robot pose estimation from a single image"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "real time 3d traffic cone detection for autonomous driving"
    - "deep high resolution representation learning for human pose estimation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "simple baselines for human pose estimation and tracking"
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"

Connected through hyperedge HE-3 (induced by source paper "efficient fully convolution neural network for generating pixel wise robotic grasps with high resolution images"):
  with members:
    - "roi based robotic grasp detection in object overlapping scenes using convolutional neural network"
    - "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.CV(Computer Vision and Pattern Recognition)