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: "color constancy by reweighting image feature maps"
  Abstract: In this study, a novel illuminant color estimation framework is proposed for color constancy, which incorporates the high representational capacity of deep-learning-based models and the great interpretability of assumption-based models. The well-designed building block, feature map reweight unit (ReWU), helps to achieve comparative accuracy on benchmark datasets with respect to prior state-of-the-art models while requiring only 1%-5% model size and 8%-20% computational cost. In addition to local color estimation, a confidence estimation branch is also included such that the model is able to produce point estimate and its uncertainty estimate simultaneously, which provides useful clues for local estimates aggregation and multiple illumination estimation. The source code and the dataset are available at this https URL.

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"
    - "revisiting gray pixel for statistical illumination estimation"
    - "green stability assumption unsupervised learning for statistics based illumination estimation"
    - "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:
    - "revisiting gray pixel for statistical illumination estimation"
    - "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"

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)