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: "improving color constancy by discounting the variation of camera spectral sensitivity"
  Abstract: It is an ill-posed problem to recover the true scene colors from a color biased image by discounting the effects of scene illuminant and camera spectral sensitivity (CSS) at the same time. Most color constancy (CC) models have been designed to first estimate the illuminant color, which is then removed from the color biased image to obtain an image taken under white light, without the explicit consideration of CSS effect on CC. This paper first studies the CSS effect on illuminant estimation arising in the inter-dataset-based CC (inter-CC), i.e., training a CC model on one dataset and then testing on another dataset captured by a distinct CSS. We show the clear degradation of existing CC models for inter-CC application. Then a simple way is proposed to overcome such degradation by first learning quickly a transform matrix between the two distinct CSSs (CSS-1 and CSS-2). The learned matrix is then used to convert the data (including the illuminant ground truth and the color-biased images) rendered under CSS-1 into CSS-2, and then train and apply the CC model on the color-biased images under CSS-2 without the need of burdensome acquiring of the training set under CSS-2. Extensive experiments on synthetic and real images show that our method can clearly improve the inter-CC performance for traditional CC algorithms. We suggest that, by taking the CSS effect into account, it is more likely to obtain the truly color constant images invariant to the changes of both illuminant and camera sensors.

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 "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"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "learning bag of features pooling for deep convolutional neural networks"

Connected through hyperedge HE-3 (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"
    - "fast fourier color constancy"
    - "convolutional color constancy"
    - "color constancy using cnns"

Connected through hyperedge HE-4 (induced by source paper "the past and the present of the color checker dataset misuse"):
  with members:
    - "revisiting gray pixel for statistical illumination estimation"
    - "color constancy by reweighting image feature maps"
    - "rehabilitating the color checker dataset for illuminant estimation"
    - "unsupervised learning for color constancy"
    - "fast fourier color constancy"
    - "convolutional color constancy"
    - "color constancy using cnns"
    - "light random sprays retinex exploiting the noisy illumination estimation"

Connected through hyperedge HE-5 (induced by source paper "color constancy convolutional autoencoder"):
  with members:
    - "rehabilitating the color checker dataset for illuminant estimation"
    - "color constancy by gans an experimental survey"
    - "a data set for camera independent color constancy"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "fast fourier color constancy"
    - "convolutional color constancy"
    - "color constancy using cnns"

Connected through hyperedge HE-6 (induced by source paper "on finding gray pixels"):
  with members:
    - "revisiting gray pixel for statistical illumination estimation"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "formulating camera adaptive color constancy as a few shot meta learning problem"):
  with members:
    - "how to train your maml"
    - "a data set for camera independent color constancy"
    - "task agnostic meta learning for few shot learning"
    - "one shot visual imitation learning via meta learning"
    - "learning transferable architectures for scalable image recognition"
    - "prototypical networks for few shot learning"
    - "meta sgd learning to learn quickly for few shot learning"
    - "unsupervised learning for color constancy"

Connected through hyperedge HE-8 (induced by source paper "revisiting gray pixel for statistical illumination estimation"):
  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"

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)