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: "convolutional color constancy"
  Abstract: Color constancy is the problem of inferring the color of the light that illuminated a scene, usually so that the illumination color can be removed. Because this problem is underconstrained, it is often solved by modeling the statistical regularities of the colors of natural objects and illumination. In contrast, in this paper we reformulate the problem of color constancy as a 2D spatial localization task in a log-chrominance space, thereby allowing us to apply techniques from object detection and structured prediction to the color constancy problem. By directly learning how to discriminate between correctly white-balanced images and poorly white-balanced images, our model is able to improve performance on standard benchmarks by nearly 40%.

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 "a benchmark for temporal color constancy"):
  with members:
    - "flash lightens gray pixels"
    - "cascading convolutional color constancy"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "fast fourier color constancy"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "color constancy by learning to predict chromaticity from luminance"

Connected through hyperedge HE-3 (induced by source paper "what else can fool deep learning addressing color constancy errors on deep neural network performance"):
  with members:
    - "sensor independent illumination estimation for dnn models"
    - "spatially transformed adversarial examples"
    - "modeling camera effects to improve deep vision for real and synthetic data"
    - "autoaugment learning augmentation policies from data"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "unprocessing images for learned raw denoising"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "random erasing data augmentation"

Connected through hyperedge HE-4 (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-5 (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"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"
    - "color constancy using cnns"

Connected through hyperedge HE-6 (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-7 (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"
    - "single and multiple illuminant estimation using convolutional neural networks"
    - "learning to compare image patches via convolutional neural networks"
    - "color constancy using cnns"

Connected through hyperedge HE-8 (induced by source paper "a dataset of multi illumination images in the wild"):
  with members:
    - "photoshape photorealistic materials for large scale shape collections"
    - "learning to predict indoor illumination from a single image"
    - "illuminant spectra based source separation using flash photography"
    - "deep outdoor illumination estimation"
    - "physically based rendering for indoor scene understanding using convolutional neural networks"
    - "natural illumination from multiple materials using deep learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "material recognition in the wild with the materials in context database"

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)