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: "novel convolution kernels for computer vision and shape analysis based on electromagnetism"
  Abstract: Computer vision is a growing field with a lot of new applications in automation and robotics, since it allows the#R##N#analysis of images and shapes for the generation of numerical or analytical information. One of the most used method#R##N#of information extraction is image filtering through convolution kernels, with each kernel specialized for specific#R##N#applications. The objective of this paper is to present a novel convolution kernels, based on principles of#R##N#electromagnetic potentials and fields, for a general use in computer vision and to demonstrate its usage for shape and#R##N#stroke analysis. Such filtering possesses unique geometrical properties that can be interpreted using well understood#R##N#physics theorems. Therefore, this paper focuses on the development of the electromagnetic kernels and on their#R##N#application on images for shape and stroke analysis. It also presents several interesting features of electromagnetic#R##N#kernels, such as resolution, size and orientation independence, robustness to noise and deformation, long distance#R##N#stroke interaction and ability to work with 3D images.

Connected through hyperedge HE-1 (induced by source paper "saliency enhancement using gradient domain edges merging"):
  with members:
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction"
    - "computing the spatial probability of inclusion inside partial contours for computer vision applications"
    - "richer convolutional features for edge detection"
    - "deep contrast learning for salient object detection"
    - "what do different evaluation metrics tell us about saliency models"
    - "visual saliency based on multiscale deep features"

Connected through hyperedge HE-2 (induced by source paper "fast and optimal laplacian solver for gradient domain image editing using green function convolution"):
  with members:
    - "computing the spatial probability of inclusion inside partial contours for computer vision applications"
    - "richer convolutional features for edge detection"
    - "fast edge detection using structured forests"

Connected through hyperedge HE-3 (induced by source paper "computing the spatial probability of inclusion inside partial contours for computer vision applications"):
  with members:
    - "texture networks feed forward synthesis of textures and stylized images"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "a dense subgraph based algorithm for compact salient image region detection"
    - "fast edge detection using structured forests"
    - "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)