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: "robust superpixels using color and contour features along linear path"
  Abstract: Superpixel decomposition methods are widely used in computer vision and image processing applications. By grouping homogeneous pixels, the accuracy can be increased and the decrease of the number of elements to process can drastically reduce the computational burden. For most superpixel methods, a trade-off is computed between 1) color homogeneity, 2) adherence to the image contours and 3) shape regularity of the decomposition. In this paper, we propose a framework that jointly enforces all these aspects and provides accurate and regular Superpixels with Contour Adherence using Linear Path (SCALP). During the decomposition, we propose to consider color features along the linear path between the pixel and the corresponding superpixel barycenter. A contour prior is also used to prevent the crossing of image boundaries when associating a pixel to a superpixel. Finally, in order to improve the decomposition accuracy and the robustness to noise, we propose to integrate the pixel neighborhood information, while preserving the same computational complexity. SCALP is extensively evaluated on standard segmentation dataset, and the obtained results outperform the ones of the state-of-the-art methods. SCALP is also extended for supervoxel decomposition on MRI images.

Connected through hyperedge HE-1 (induced by source paper "texture superpixel clustering from patch based nearest neighbor matching"):
  with members:
    - "superpatchmatch an algorithm for robust correspondences using superpixel patches"
    - "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-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"
    - "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-3 (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"
    - "superpixel convolutional networks using bilateral inceptions"
    - "gasp geometric association with surface patches"

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)