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 "crop color constancy benchmark dataset generator"):
  Abstract: Implementing color constancy as a pre-processing step in contemporary digital cameras is of significant importance as it removes the influence of scene illumination on object colors. Several benchmark color constancy datasets have been created for the purpose of developing and testing new color constancy methods. However, they all have numerous drawbacks including a small number of images, erroneously extracted ground-truth illuminations, long histories of misuses, violations of their stated assumptions, etc. To overcome such and similar problems, in this paper a color constancy benchmark dataset generator is proposed. For a given camera sensor it enables generation of any number of realistic raw images taken in a subset of the real world, namely images of printed photographs. Datasets with such images share many positive features with other existing real-world datasets, while some of the negative features are completely eliminated. The generated images can be successfully used to train methods that afterward achieve high accuracy on real-world datasets. This opens the way for creating large enough datasets for advanced deep learning techniques. Experimental results are presented and discussed. The source code is available at this http URL.

Includes members:
  Member "the past and the present of the color checker dataset misuse":
    also connected through other hyperedges:
      (none)
  Member "self adaptive single and multi illuminant estimation framework based on deep learning":
    also connected through other hyperedges:
      (none)
  Member "revisiting gray pixel for statistical illumination estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the past and the present of the color checker dataset misuse")
  Member "green stability assumption unsupervised learning for statistics based illumination estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised learning for color constancy")
  Member "color constancy by reweighting image feature maps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the past and the present of the color checker dataset misuse")
  Member "rehabilitating the color checker dataset for illuminant estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bag of color features for color constancy")
  Member "a data set for camera independent color constancy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bag of color features for color constancy")
  Member "unsupervised learning for color constancy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a multi hypothesis approach to color constancy")

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)