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: "a data set for camera independent color constancy"
  Abstract: In this paper, we provide a novel data set designed for Camera-independent color constancy research. Camera independence corresponds to the robustness of an algorithm’s performance when it runs on images of the same scene taken by different cameras. Accordingly, the images in our database correspond to several laboratory and field scenes each of which is captured by three different cameras with minimal registration errors. The laboratory scenes are also captured under five different illuminations. The spectral responses of cameras and the spectral power distributions of the laboratory light sources are also provided, as they may prove beneficial for training future algorithms to achieve color constancy. For a fair evaluation of future methods, we provide guidelines for supervised methods with indicated training, validation, and testing partitions. Accordingly, we evaluate two recently proposed convolutional neural network-based color constancy algorithms as baselines for future research. As a side contribution, this data set also includes images taken by a mobile camera with color shading corrected and uncorrected results. This allows research on the effect of color shading as well.

Connected through hyperedge HE-1 (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"
    - "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"
    - "learning bag of features pooling for deep convolutional neural networks"

Connected through hyperedge HE-2 (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"
    - "unsupervised learning for color constancy"
    - "fast fourier color constancy"

Connected through hyperedge HE-3 (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"
    - "improving color constancy by discounting the variation of camera spectral sensitivity"
    - "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-4 (induced by source paper "formulating camera adaptive color constancy as a few shot meta learning problem"):
  with members:
    - "how to train your maml"
    - "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"
    - "improving color constancy by discounting the variation of camera spectral sensitivity"

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)