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: "fast fourier color constancy"
  Abstract: We present Fast Fourier Color Constancy (FFCC), a color constancy algorithm which solves illuminant estimation by reducing it to a spatial localization task on a torus. By operating in the frequency domain, FFCC produces lower error rates than the previous state-of-the-art by 13-20% while being 250-3000 times faster. This unconventional approach introduces challenges regarding aliasing, directional statistics, and preconditioning, which we address. By producing a complete posterior distribution over illuminants instead of a single illuminant estimate, FFCC enables better training techniques, an effective temporal smoothing technique, and richer methods for error analysis. Our implementation of FFCC runs at ~700 frames per second on a mobile device, allowing it to be used as an accurate, real-time, temporally-coherent automatic white balance algorithm.

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"
    - "convolutional 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 "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 "mimicking the in camera color pipeline for camera aware object compositing"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "modelling the scene dependent imaging in cameras with a deep neural network"
    - "deep photo style transfer"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep image harmonization"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep outdoor illumination estimation"

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"
    - "iterative instance segmentation"
    - "convolutional color constancy"
    - "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 "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"

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)