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: "superpatchmatch an algorithm for robust correspondences using superpixel patches"
  Abstract: Superpixels have become very popular in many computer vision applications. Nevertheless, they remain underexploited since the superpixel decomposition may produce irregular and non stable segmentation results due to the dependency to the image content. In this paper, we first introduce a novel structure, a superpixel-based patch, called SuperPatch. The proposed structure, based on superpixel neighborhood, leads to a robust descriptor since spatial information is naturally included. The generalization of the PatchMatch method to SuperPatches, named SuperPatchMatch, is introduced. Finally, we propose a framework to perform fast segmentation and labeling from an image database, and demonstrate the potential of our approach since we outperform, in terms of computational cost and accuracy, the results of state-of-the-art methods on both face labeling and medical image segmentation.

Connected through hyperedge HE-1 (induced by source paper "multi scale superpatch matching using dual superpixel descriptors"):
  with members:
    - "texture superpixel clustering from patch based nearest neighbor matching"
    - "evaluation framework of superpixel methods with a global regularity measure"
    - "superpixels an evaluation of the state of the art"
    - "universal adversarial perturbations"
    - "u net convolutional networks for biomedical image segmentation"
    - "gasp geometric association with surface patches"

Connected through hyperedge HE-2 (induced by source paper "texture superpixel clustering from patch based nearest neighbor matching"):
  with members:
    - "robust superpixels using color and contour features along linear path"
    - "superpixels an evaluation of the state of the art"
    - "photo stylistic brush robust style transfer via superpixel based bipartite graph"
    - "feedforward semantic segmentation with zoom out features"

Connected through hyperedge HE-3 (induced by source paper "evaluation framework of superpixel methods with a global regularity measure"):
  with members:
    - "robust shape regularity criteria for superpixel evaluation"
    - "robust superpixels using color and contour features along linear path"
    - "superpixels an evaluation of the state of the art"
    - "superpixel convolutional networks using bilateral inceptions"
    - "gasp geometric association with surface patches"
    - "deep convolutional neural fields for depth estimation from a single image"

Connected through hyperedge HE-4 (induced by source paper "robust superpixels using color and contour features along linear path"):
  with members:
    - "evaluation framework of superpixel methods with a global regularity measure"
    - "robust shape regularity criteria for superpixel evaluation"
    - "scalp superpixels with contour adherence using linear path"
    - "superpixels an evaluation of the state of the art"
    - "gasp geometric association with surface patches"
    - "seeds superpixels extracted via energy driven sampling"

Connected through hyperedge HE-5 (induced by source paper "texture aware superpixel segmentation"):
  with members:
    - "evaluation framework of superpixel methods with a global regularity measure"
    - "robust superpixels using color and contour features along linear path"
    - "superpixel convolutional networks using bilateral inceptions"
    - "gasp geometric association with surface patches"

Connected through hyperedge HE-6 (induced by source paper "superpixel based color transfer"):
  with members:
    - "scalp superpixels with contour adherence using linear path"
    - "photo stylistic brush robust style transfer via superpixel based bipartite graph"
    - "regularized discrete optimal transport"

Connected through hyperedge HE-7 (induced by source paper "unsupervised learning based long term superpixel tracking"):
  with members:
    - "superpixels an evaluation of the state of the art"
    - "deep reinforcement learning for visual object tracking in videos"
    - "deeptrack learning discriminative feature representations online for robust visual tracking"

Connected through hyperedge HE-8 (induced by source paper "robust shape regularity criteria for superpixel evaluation"):
  with members:
    - "scalp superpixels with contour adherence using linear path"
    - "superpixels an evaluation of the state of the art"

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)