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 "hybrid noise removal in hyperspectral imagery with a spatial spectral gradient network"):
  Abstract: The existence of hybrid noise in hyperspectral images (HSIs) severely degrades the data quality, reduces the interpretation accuracy of HSIs, and restricts the subsequent HSIs applications. In this paper, the spatial-spectral gradient network (SSGN) is presented for mixed noise removal in HSIs. The proposed method employs a spatial-spectral gradient learning strategy, in consideration of the unique spatial structure directionality of sparse noise and spectral differences with additional complementary information for better extracting intrinsic and deep features of HSIs. Based on a fully cascaded multi-scale convolutional network, SSGN can simultaneously deal with the different types of noise in different HSIs or spectra by the use of the same model. The simulated and real-data experiments undertaken in this study confirmed that the proposed SSGN performs better at mixed noise removal than the other state-of-the-art HSI denoising algorithms, in evaluation indices, visual assessments, and time consumption.

Includes members:
  Member "hyperspectral image denoising employing a spatial spectral deep residual convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d quasi recurrent neural network for hyperspectral image denoising")
  Member "target adaptive cnn based pansharpening":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "s3 a spectral spatial structure loss for pan sharpening networks")
  Member "missing data reconstruction in remote sensing image with a unified spatial temporal spectral deep convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning inversion of electrical resistivity data")
  Member "boosting the accuracy of multispectral image pansharpening by learning a deep residual network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised and unregistered hyperspectral image super resolution with mutual dirichlet net")
  Member "aid a benchmark data set for performance evaluation of aerial scene classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "agriculture vision a large aerial image database for agricultural pattern analysis")
  Member "learning a dilated residual network for sar image despeckling":
    also connected through other hyperedges:
      (none)
  Member "beyond a gaussian denoiser residual learning of deep cnn for image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "denoising hyperspectral image with non i i d noise structure":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "3d quasi recurrent neural network for hyperspectral image denoising")

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)