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: "light random sprays retinex exploiting the noisy illumination estimation"
  Abstract: In this letter, Light Random Sprays Retinex (LRSR), an improvement of the Random Sprays Retinex (RSR) algorithm is proposed. RSR is a white balancing algorithm for achieving local color constancy and image enhancement by using random sprays of the same size. The main problem of the original RSR is that the lower the number and size of the sprays, the greater the noise in the resulting image, which means that the number and size of sprays have to be relatively high in order to reduce the noise leading to a higher computation cost. The proposed improved algorithm is based on a new method to remove the noise in the resulting image thereby allowing only one spray of a smaller size to be used resulting in lower computation cost. By using interpolation the computation cost is reduced even further without a noticeable perceptual difference. The improvement is tested on a public database and is shown to outperform the original RSR in image quality and computation cost. The source code is available at http://www.fer.unizg.hr/ipg/resources/color_constancy/.

Connected through hyperedge HE-1 (induced by source paper "the past and the present of the color checker dataset misuse"):
  with members:
    - "revisiting gray pixel for statistical illumination estimation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning for color constancy"):
  with members:
    - "green stability assumption unsupervised learning for statistics based illumination estimation"
    - "rehabilitating the color checker dataset for illuminant estimation"
    - "approaching the computational color constancy as a classification problem through deep learning"
    - "improving color constancy by discounting the variation of camera spectral sensitivity"
    - "fast fourier color constancy"
    - "convolutional color constancy"
    - "color constancy using cnns"

Connected through hyperedge HE-3 (induced by source paper "on the duality between retinex and image dehazing"):
  with members:
    - "dehazenet an end to end system for single image haze removal"
    - "haze visibility enhancement a survey and quantitative benchmarking"

Connected through hyperedge HE-4 (induced by source paper "single and multiple illuminant estimation using convolutional neural networks"):
  with members:
    - "approaching the computational color constancy as a classification problem through deep learning"
    - "convolutional color constancy"
    - "color constancy using cnns"
    - "color constancy by learning to predict chromaticity from luminance"
    - "caffe convolutional architecture for fast feature embedding"

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)