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: "approaching the computational color constancy as a classification problem through deep learning"
  Abstract: Abstract   Computational color constancy refers to the problem of computing the illuminant color so that the images of a scene under varying illumination can be normalized to an image under the canonical illumination. In this paper, we adopt a deep learning framework for the illumination estimation problem. The proposed method works under the assumption of uniform illumination over the scene and aims for the accurate illuminant color computation. Specifically, we trained the convolutional neural network to solve the problem by casting the color constancy problem as an illumination classification problem. We designed the deep learning architecture so that the output of the network can be directly used for computing the color of the illumination. Experimental results show that our deep network is able to extract useful features for the illumination estimation and our method outperforms all previous color constancy methods on multiple test datasets.

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"
    - "unsupervised learning for color constancy"

Connected through hyperedge HE-2 (induced by source paper "local area transform for cross modality correspondence matching and deep scene recognition"):
  with members:
    - "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"
    - "color constancy using cnns"

Connected through hyperedge HE-3 (induced by source paper "deepilluminance contextual illuminance estimation via deep neural networks"):
  with members:
    - "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"
    - "color constancy using cnns"

Connected through hyperedge HE-4 (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"
    - "unsupervised learning for color constancy"
    - "improving color constancy by discounting the variation of camera spectral sensitivity"
    - "fast fourier color constancy"
    - "convolutional color constancy"
    - "color constancy using cnns"

Connected through hyperedge HE-5 (induced by source paper "learning illuminant estimation from object recognition"):
  with members:
    - "deep learning for logo recognition"
    - "color constancy using cnns"

Connected through hyperedge HE-6 (induced by source paper "a data set for camera independent color constancy"):
  with members:
    - "improving color constancy by discounting the variation of camera spectral sensitivity"
    - "color constancy using cnns"

Connected through hyperedge HE-7 (induced by source paper "learning with rethinking recurrently improving convolutional neural networks through feedback"):
  with members:
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "seven ways to improve example based single image super resolution"
    - "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character recognition"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "deeply recursive convolutional network for image super resolution"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-8 (induced by source paper "modelling the scene dependent imaging in cameras with a deep neural network"):
  with members:
    - "learnable histogram statistical context features for deep neural networks"
    - "learning representations for automatic colorization"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "colorful image colorization"
    - "pixelnet towards a general pixel level architecture"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

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)