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 "on finding gray pixels"):
  Abstract: We propose a novel grayness index for finding gray pixels and demonstrate its effectiveness and efficiency in illumination estimation. The grayness index, GI in short, is derived using the Dichromatic Reflection Model and is learning-free. GI allows to estimate one or multiple illumination sources in color-biased images. On standard single-illumination and multiple-illumination estimation benchmarks, GI outperforms state-of-the-art statistical methods and many recent deep methods. GI is simple and fast, written in a few dozen lines of code, processing a 1080p image in ~0.4 seconds with a non-optimized Matlab code.

Includes members:
  Member "revisiting gray pixel for statistical illumination estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "crop color constancy benchmark dataset generator")
  Member "improving color constancy by discounting the variation of camera spectral sensitivity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a multi hypothesis approach to color constancy")
  Member "deep structured output regression learning for computational color constancy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cascading convolutional color constancy")
  Member "fast fourier color constancy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a multi hypothesis approach to color constancy")
  Member "convolutional color constancy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a multi hypothesis approach to color constancy")
  Member "color constancy by learning to predict chromaticity from luminance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a benchmark for temporal 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)