human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "superpatchmatch an algorithm for robust correspondences using superpixel patches":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi scale superpatch matching using dual superpixel descriptors")
  Member "robust shape regularity criteria for superpixel evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust superpixels using color and contour features along linear path")
  Member "robust superpixels using color and contour features along linear path":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "texture superpixel clustering from patch based nearest neighbor matching")
  Member "superpixels an evaluation of the state of the art":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi scale superpatch matching using dual superpixel descriptors")
  Member "superpixel convolutional networks using bilateral inceptions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gated scnn gated shape cnns for semantic segmentation")
  Member "gasp geometric association with surface patches":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi scale superpatch matching using dual superpixel descriptors")
  Member "deep convolutional neural fields for depth estimation from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "predicting sharp and accurate occlusion boundaries in monocular depth estimation using displacement fields")

Question: Please tell me which class the source paper 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)