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: "single and multiple illuminant estimation using convolutional neural networks"
  Abstract: In this paper we present a method for the estimation of the color of the illuminant in RAW images. The method includes a Convolutional Neural Network that has been specially designed to produce multiple local estimates. A multiple illuminant detector determines whether or not the local outputs of the network must be aggregated into a single estimate. We evaluated our method on standard datasets with single and multiple illuminants, obtaining lower estimation errors with respect to those obtained by other general purpose methods in the state of the art.

Connected through hyperedge HE-1 (induced by source paper "a multi hypothesis approach to color constancy"):
  with members:
    - "sensor independent illumination estimation for dnn models"
    - "color cerberus"
    - "what else can fool deep learning addressing color constancy errors on deep neural network performance"
    - "fast fourier color constancy and grayness index for ispa illumination estimation challenge"
    - "convolutional mean a simple convolutional neural network for illuminant estimation"
    - "modeling camera effects to improve deep vision for real and synthetic data"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "approaching the computational color constancy as a classification problem through deep learning"

Connected through hyperedge HE-2 (induced by source paper "deepilluminance contextual illuminance estimation via deep neural networks"):
  with members:
    - "approaching the computational color constancy as a classification problem through deep learning"
    - "joint material and illumination estimation from photo sets in the wild"
    - "stacked hourglass networks for human pose estimation"
    - "fast fourier color constancy"
    - "iterative instance segmentation"
    - "convolutional color constancy"
    - "learning to compare image patches via convolutional neural networks"
    - "color constancy using cnns"

Connected through hyperedge HE-3 (induced by source paper "self adaptive single and multi illuminant estimation framework based on deep learning"):
  with members:
    - "unsupervised learning for color constancy"
    - "burst denoising with kernel prediction networks"
    - "fast fourier color constancy"
    - "convolutional color constancy"
    - "u net convolutional networks for biomedical image segmentation"
    - "color constancy using cnns"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "convolutional mean a simple convolutional neural network for illuminant estimation"):
  with members:
    - "fast fourier color constancy"
    - "convolutional color constancy"
    - "color constancy using cnns"
    - "color constancy by learning to predict chromaticity from luminance"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "dense color constancy with effective edge augmentation"):
  with members:
    - "densely connected convolutional networks"
    - "fast fourier color constancy"
    - "convolutional color constancy"
    - "color constancy using cnns"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "cascading convolutional color constancy"):
  with members:
    - "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"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "fast color constancy with patch wise bright pixels"):
  with members:
    - "fast fourier color constancy"
    - "convolutional color constancy"
    - "color constancy using cnns"
    - "color constancy by learning to predict chromaticity from luminance"

Connected through hyperedge HE-8 (induced by source paper "multiple light source dataset for colour research"):
  with members:
    - "linear colour segmentation revisited"
    - "unsupervised learning for color constancy"
    - "sfsnet learning shape reflectance and illuminance of faces in the wild"

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)