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: "evaluation framework of superpixel methods with a global regularity measure"
  Abstract: In the superpixel literature, the comparison of state-of-the-art methods can be biased by the non-robustness of some metrics to decomposition aspects, such as the superpixel scale. Moreover, most recent decomposition methods allow to set a shape regularity parameter, which can have a substantial impact on the measured performances. In this paper, we introduce an evaluation framework, that aims to unify the comparison process of superpixel methods. We investigate the limitations of existing metrics, and propose to evaluate each of the three core decomposition aspects: color homogeneity, respect of image objects and shape regularity. To measure the regularity aspect, we propose a new global regularity measure (GR), which addresses the non-robustness of state-of-the-art metrics. We evaluate recent superpixel methods with these criteria, at several superpixel scales and regularity levels. The proposed framework reduces the bias in the comparison process of state-of-the-art superpixel methods. Finally, we demonstrate that the proposed GR measure is correlated with the performances of various applications.

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"
    - "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"
    - "gasp geometric association with surface patches"

Connected through hyperedge HE-2 (induced by source paper "robust superpixels using color and contour features along linear path"):
  with members:
    - "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"
    - "gasp geometric association with surface patches"
    - "seeds superpixels extracted via energy driven sampling"

Connected through hyperedge HE-3 (induced by source paper "texture aware superpixel segmentation"):
  with members:
    - "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"
    - "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)