human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "unsupervised learning for color constancy"):
  Abstract: Most digital camera pipelines use color constancy methods to reduce the influence of illumination and camera sensor on the colors of scene objects. The highest accuracy of color correction is obtained with learning-based color constancy methods, but they require a significant amount of calibrated training images with known ground-truth illumination. Such calibration is time consuming, preferably done for each sensor individually, and therefore a major bottleneck in acquiring high color constancy accuracy. Statistics-based methods do not require calibrated training images, but they are less accurate. In this paper an unsupervised learning-based method is proposed that learns its parameter values after approximating the unknown ground-truth illumination of the training images, thus avoiding calibration. In terms of accuracy the proposed method outperforms all statistics-based and many learning-based methods. An extension of the method is also proposed, which learns the needed parameters from non-calibrated images taken with one sensors and which can then be successfully applied to images taken with another sensor. This effectively enables inter-camera unsupervised learning for color constancy. Additionally, a new high quality color constancy benchmark dataset with 1365 calibrated images is created, used for testing, and made publicly available. The results are presented and discussed. The source code and the dataset are available at this http URL

Includes members:
  Member "green stability assumption unsupervised learning for statistics based illumination estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "crop color constancy benchmark dataset generator")
  Member "rehabilitating the color checker dataset for illuminant estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bag of color features for color constancy")
  Member "approaching the computational color constancy as a classification problem through deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a multi hypothesis approach to color constancy")
  Member "improving color constancy by discounting the variation of camera spectral sensitivity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a multi hypothesis approach to color constancy")
  Member "fast fourier color constancy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a multi hypothesis approach to color constancy")
  Member "convolutional color constancy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a multi hypothesis approach to color constancy")
  Member "color constancy using cnns":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "replacing mobile camera isp with a single deep learning model")
  Member "light random sprays retinex exploiting the noisy illumination estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the past and the present of the color checker dataset misuse")

Question: Please tell me which class the source paper 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)