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 using cnns"
  Abstract: In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max pooling, one fully connected layer and three output nodes. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating scene illumination. This approach achieves state-of-the-art performance on a standard dataset of RAW images. Preliminary experiments on images with spatially varying illumination demonstrate the stability of the local illuminant estimation ability of our CNN.

Connected through hyperedge HE-1 (induced by source paper "replacing mobile camera isp with a single deep learning model"):
  with members:
    - "ai benchmark all about deep learning on smartphones in 2019"
    - "aim 2019 challenge on image demoireing dataset and study"
    - "semantic image synthesis with spatially adaptive normalization"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "nice noise injection and clamping estimation for neural network quantization"
    - "ai benchmark running deep neural networks on android smartphones"
    - "fast perceptual image enhancement"

Connected through hyperedge HE-2 (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-3 (induced by source paper "bag of color features for color constancy"):
  with members:
    - "color constancy convolutional autoencoder"
    - "rehabilitating the color checker dataset for illuminant estimation"
    - "color constancy by gans an experimental survey"
    - "a data set for camera independent color constancy"
    - "attention is all you need"
    - "massive exploration of neural machine translation architectures"
    - "improving color constancy by discounting the variation of camera spectral sensitivity"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-4 (induced by source paper "local area transform for cross modality correspondence matching and deep scene recognition"):
  with members:
    - "approaching the computational color constancy as a classification problem through deep learning"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "scnet learning semantic correspondence"
    - "semantic clustering for robust fine grained scene recognition"
    - "deep fishernet for object classification"
    - "convolutional color constancy"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "star a structure and texture aware retinex model"):
  with members:
    - "noisy as clean learning unsupervised denoising from the corrupted image"
    - "nlh a blind pixel level non local method for real world image denoising"
    - "salient object detection a survey"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "simultaneous fidelity and regularization learning for image restoration"
    - "scaled simplex representation for subspace clustering"
    - "color constancy by gans an experimental survey"
    - "external prior guided internal prior learning for real world noisy image denoising"

Connected through hyperedge HE-6 (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"
    - "single and multiple illuminant estimation using convolutional neural networks"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-7 (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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "sensor independent illumination estimation for dnn models"):
  with members:
    - "on finding gray pixels"
    - "formulating camera adaptive color constancy as a few shot meta learning problem"
    - "meta learning for few shot camera adaptive color constancy"
    - "approaching the computational color constancy as a classification problem through deep learning"
    - "unsupervised learning for color constancy"
    - "improving color constancy by discounting the variation of camera spectral sensitivity"
    - "fast fourier color constancy"
    - "convolutional color constancy"

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)