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: "scalp superpixels with contour adherence using linear path"
  Abstract: Superpixel decomposition methods are generally used as a pre-processing step to speed up image processing tasks. They group the pixels of an image into homogeneous regions while trying to respect existing contours. For all state-of-the-art superpixel decomposition methods, a trade-off is made between 1) computational time, 2) adherence to image contours and 3) regularity and compactness of the decomposition. In this paper, we propose a fast method to compute Superpixels with Contour Adherence using Linear Path (SCALP) in an iterative clustering framework. The distance computed when trying to associate a pixel to a superpixel during the clustering is enhanced by considering the linear path to the superpixel barycenter. The proposed framework produces regular and compact superpixels that adhere to the image contours. We provide a detailed evaluation of SCALP on the standard Berkeley Segmentation Dataset. The obtained results outperform state-of-the-art methods in terms of standard superpixel and contour detection metrics.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "superpatchmatch an algorithm for robust correspondences using superpixel patches"):
  with members:
    - "photo stylistic brush robust style transfer via superpixel based bipartite graph"
    - "feedforward semantic segmentation with zoom out features"
    - "gasp geometric association with surface patches"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "superpixel based color transfer"):
  with members:
    - "superpatchmatch an algorithm for robust correspondences using superpixel patches"
    - "photo stylistic brush robust style transfer via superpixel based bipartite graph"
    - "regularized discrete optimal transport"

Connected through hyperedge HE-4 (induced by source paper "robust shape regularity criteria for superpixel evaluation"):
  with members:
    - "superpatchmatch an algorithm for robust correspondences using superpixel patches"
    - "superpixels an evaluation of the state of the art"

Connected through hyperedge HE-5 (induced by source paper "superpixel sampling networks"):
  with members:
    - "clustering with deep learning taxonomy and new methods"
    - "neural expectation maximization"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "tagger deep unsupervised perceptual grouping"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "gslicr slic superpixels at over 250hz"
    - "superpixel convolutional networks using bilateral inceptions"
    - "deep clustering discriminative embeddings for segmentation and separation"

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)