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: "revisiting gray pixel for statistical illumination estimation"
  Abstract: We present a statistical color constancy method that relies on novel gray pixel detection and mean shift clustering. The method, called Mean Shifted Grey Pixel -- MSGP, is based on the observation: true-gray pixels are aligned towards one single direction. Our solution is compact, easy to compute and requires no training. Experiments on two real-world benchmarks show that the proposed approach outperforms state-of-the-art methods in the camera-agnostic scenario. In the setting where the camera is known, MSGP outperforms all statistical methods.

Connected through hyperedge HE-1 (induced by source paper "crop color constancy benchmark dataset generator"):
  with members:
    - "the past and the present of the color checker dataset misuse"
    - "self adaptive single and multi illuminant estimation framework based on deep learning"
    - "green stability assumption unsupervised learning for statistics based illumination estimation"
    - "color constancy by reweighting image feature maps"
    - "rehabilitating the color checker dataset for illuminant estimation"
    - "a data set for camera independent color constancy"
    - "unsupervised learning for color constancy"
    - "fast fourier color constancy"

Connected through hyperedge HE-2 (induced by source paper "the past and the present of the color checker dataset misuse"):
  with members:
    - "color constancy by reweighting image feature maps"
    - "rehabilitating the color checker dataset for illuminant estimation"
    - "unsupervised learning for color constancy"
    - "improving color constancy by discounting the variation of camera spectral sensitivity"
    - "fast fourier color constancy"
    - "convolutional color constancy"
    - "color constancy using cnns"
    - "light random sprays retinex exploiting the noisy illumination estimation"

Connected through hyperedge HE-3 (induced by source paper "fast fourier color constancy and grayness index for ispa illumination estimation challenge"):
  with members:
    - "color cerberus"
    - "on finding gray pixels"
    - "unsupervised learning for color constancy"
    - "places an image database for deep scene understanding"
    - "fast fourier color constancy"
    - "convolutional color constancy"

Connected through hyperedge HE-4 (induced by source paper "on finding gray pixels"):
  with members:
    - "improving color constancy by discounting the variation of camera spectral sensitivity"
    - "deep structured output regression learning for computational color constancy"
    - "fast fourier color constancy"
    - "convolutional color constancy"
    - "color constancy by learning to predict chromaticity from luminance"

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)