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: "gasp geometric association with surface patches"
  Abstract: A fundamental challenge to sensory processing tasks in perception and robotics is the problem of obtaining data associations across views. We present a robust solution for ascertaining potentially dense surface patch (superpixel) associations, requiring just range information. Our approach involves decomposition of a view into regularized surface patches. We represent them as sequences expressing geometry invariantly over their superpixel neighborhoods, as uniquely consistent partial orderings. We match these representations through an optimal sequence comparison metric based on the Damerau-Levenshtein distance - enabling robust association with quadratic complexity (in contrast to hitherto employed joint matching formulations which are NP-complete). The approach is able to perform under wide baselines, heavy rotations, partial overlaps, significant occlusions and sensor noise. #R##N#The technique does not require any priors -- motion or otherwise, and does not make restrictive assumptions on scene structure and sensor movement. It does not require appearance -- is hence more widely applicable than appearance reliant methods, and invulnerable to related ambiguities such as textureless or aliased content. We present promising qualitative and quantitative results under diverse settings, along with comparatives with popular approaches based on range as well as RGB-D data.

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"
    - "superpatchmatch an algorithm for robust correspondences using superpixel patches"
    - "superpixels an evaluation of the state of the art"
    - "universal adversarial perturbations"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "evaluation framework of superpixel methods with a global regularity measure"):
  with members:
    - "superpatchmatch an algorithm for robust correspondences using superpixel patches"
    - "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"
    - "deep convolutional neural fields for depth estimation from a single image"

Connected through hyperedge HE-3 (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"
    - "superpatchmatch an algorithm for robust correspondences using superpixel patches"
    - "robust shape regularity criteria for superpixel evaluation"
    - "scalp superpixels with contour adherence using linear path"
    - "superpixels an evaluation of the state of the art"
    - "seeds superpixels extracted via energy driven sampling"

Connected through hyperedge HE-4 (induced by source paper "texture aware superpixel segmentation"):
  with members:
    - "evaluation framework of superpixel methods with a global regularity measure"
    - "superpatchmatch an algorithm for robust correspondences using superpixel patches"
    - "robust superpixels using color and contour features along linear path"
    - "superpixel convolutional networks using bilateral inceptions"

Connected through hyperedge HE-5 (induced by source paper "superpatchmatch an algorithm for robust correspondences using superpixel patches"):
  with members:
    - "scalp superpixels with contour adherence using linear path"
    - "photo stylistic brush robust style transfer via superpixel based bipartite graph"
    - "feedforward semantic segmentation with zoom out features"
    - "fully convolutional networks for semantic segmentation"

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)