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: "systematic study of color spaces and components for the segmentation of sky cloud images"
  Abstract: Sky/cloud imaging using ground-based Whole Sky Imagers (WSI) is a cost-effective means to understanding cloud cover and weather patterns. The accurate segmentation of clouds in these images is a challenging task, as clouds do not possess any clear structure. Several algorithms using different color models have been proposed in the literature. This paper presents a systematic approach for the selection of color spaces and components for optimal segmentation of sky/cloud images. Using mainly principal component analysis (PCA) and fuzzy clustering for evaluation, we identify the most suitable color components for this task.

Connected through hyperedge HE-1 (induced by source paper "multi label cloud segmentation using a deep network"):
  with members:
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "high dynamic range imaging for cloud segmentation"):
  with members:
    - "design of low cost compact and weather proof whole sky imagers for high dynamic range captures"
    - "rough set based color channel selection"
    - "estimation of solar irradiance using ground based whole sky imagers"

Connected through hyperedge HE-3 (induced by source paper "nighttime sky cloud image segmentation"):
  with members:
    - "design of low cost compact and weather proof whole sky imagers for high dynamic range captures"
    - "color based segmentation of sky cloud images from ground based cameras"

Connected through hyperedge HE-4 (induced by source paper "rough set based color channel selection"):
  with members:
    - "design of low cost compact and weather proof whole sky imagers for high dynamic range captures"
    - "color based segmentation of sky cloud images from ground based cameras"

Connected through hyperedge HE-5 (induced by source paper "correlating satellite cloud cover with sky cameras"):
  with members:
    - "design of low cost compact and weather proof whole sky imagers for high dynamic range captures"
    - "analyzing cloud optical properties using sky cameras"

Connected through hyperedge HE-6 (induced by source paper "short term prediction of localized cloud motion using ground based sky imagers"):
  with members:
    - "design of low cost compact and weather proof whole sky imagers for high dynamic range captures"

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)