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: "color constancy by learning to predict chromaticity from luminance"
  Abstract: Color constancy is the recovery of true surface color from observed color, and requires estimating the chromaticity of scene illumination to correct for the bias it induces. In this paper, we show that the per-pixel color statistics of natural scenes---without any spatial or semantic context---can by themselves be a powerful cue for color constancy. Specifically, we describe an illuminant estimation method that is built around a "classifier" for identifying the true chromaticity of a pixel given its luminance (absolute brightness across color channels). During inference, each pixel's observed color restricts its true chromaticity to those values that can be explained by one of a candidate set of illuminants, and applying the classifier over these values yields a distribution over the corresponding illuminants. A global estimate for the scene illuminant is computed through a simple aggregation of these distributions across all pixels. We begin by simply defining the luminance-to-chromaticity classifier by computing empirical histograms over discretized chromaticity and luminance values from a training set of natural images. These histograms reflect a preference for hues corresponding to smooth reflectance functions, and for achromatic colors in brighter pixels. Despite its simplicity, the resulting estimation algorithm outperforms current state-of-the-art color constancy methods. Next, we propose a method to learn the luminance-to-chromaticity classifier "end-to-end". Using stochastic gradient descent, we set chromaticity-luminance likelihoods to minimize errors in the final scene illuminant estimates on a training set. This leads to further improvements in accuracy, most significantly in the tail of the error distribution.

Connected through hyperedge HE-1 (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 color constancy"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-2 (induced by source paper "convolutional mean a simple convolutional neural network for illuminant estimation"):
  with members:
    - "fast fourier color constancy"
    - "convolutional color constancy"
    - "single and multiple illuminant estimation using convolutional neural networks"
    - "color constancy using cnns"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "on finding gray pixels"):
  with members:
    - "revisiting gray pixel for statistical illumination estimation"
    - "improving color constancy by discounting the variation of camera spectral sensitivity"
    - "deep structured output regression learning for computational color constancy"
    - "fast fourier color constancy"
    - "convolutional color constancy"

Connected through hyperedge HE-4 (induced by source paper "cascading convolutional color constancy"):
  with members:
    - "deep structured output regression learning for computational color constancy"
    - "fast fourier color constancy"
    - "convolutional color constancy"
    - "single and multiple illuminant estimation using convolutional neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "fast color constancy with patch wise bright pixels"):
  with members:
    - "fast fourier color constancy"
    - "convolutional color constancy"
    - "single and multiple illuminant estimation using convolutional neural networks"
    - "color constancy using cnns"

Connected through hyperedge HE-6 (induced by source paper "revisiting gray pixel for statistical illumination estimation"):
  with members:
    - "improving color constancy by discounting the variation of camera spectral sensitivity"
    - "deep structured output regression learning for computational color constancy"
    - "fast fourier color constancy"
    - "convolutional color constancy"

Connected through hyperedge HE-7 (induced by source paper "approaching the computational color constancy as a classification problem through deep learning"):
  with members:
    - "color constancy using cnns"
    - "understanding deep image representations by inverting them"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "depth map prediction from a single image using a multi scale deep network"
    - "fully convolutional networks for semantic segmentation"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "illuminant spectra based source separation using flash photography"):
  with members:
    - "learning to predict indoor illumination from a single image"
    - "deep outdoor illumination estimation"
    - "single image rgb photometric stereo with spatially varying albedo"
    - "convolutional 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)