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: "hyperspectral image denoising employing a spatial spectral deep residual convolutional neural network"
  Abstract: Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the performance of the subsequent HSI interpretation and applications. In this paper, a novel deep learning-based method for this task is proposed, by learning a nonlinear end-to-end mapping between the noisy and clean HSIs with a combined spatial–spectral deep convolutional neural network (HSID-CNN). Both the spatial and spectral information are simultaneously assigned to the proposed network. In addition, multiscale feature extraction and multilevel feature representation are, respectively, employed to capture both the multiscale spatial–spectral feature and fuse different feature representations for the final restoration. The simulated and real-data experiments demonstrate that the proposed HSID-CNN outperforms many of the mainstream methods in both the quantitative evaluation indexes, visual effects, and HSI classification accuracy.

Connected through hyperedge HE-1 (induced by source paper "3d quasi recurrent neural network for hyperspectral image denoising"):
  with members:
    - "residual non local attention networks for image restoration"
    - "residual dense network for image restoration"
    - "non local meets global an integrated paradigm for hyperspectral denoising"
    - "weighted low rank tensor recovery for hyperspectral image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "denoising hyperspectral image with non i i d noise structure"
    - "memnet a persistent memory network for image restoration"
    - "quasi recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "deep hyperspectral prior denoising inpainting super resolution"):
  with members:
    - "generative image inpainting with contextual attention"
    - "deep image prior"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "generalized inpainting method for hyperspectral image acquisition"
    - "empirical evaluation of rectified activations in convolutional network"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "constrained low tubal rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections"):
  with members:
    - "exact tensor completion using t svd"

Connected through hyperedge HE-4 (induced by source paper "hybrid noise removal in hyperspectral imagery with a spatial spectral gradient network"):
  with members:
    - "target adaptive cnn based pansharpening"
    - "missing data reconstruction in remote sensing image with a unified spatial temporal spectral deep convolutional neural network"
    - "boosting the accuracy of multispectral image pansharpening by learning a deep residual network"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "learning a dilated residual network for sar image despeckling"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "denoising hyperspectral image with non i i d noise structure"
    - "image restoration using convolutional auto encoders with symmetric skip connections"

Connected through hyperedge HE-5 (induced by source paper "non local meets global an integrated paradigm for hyperspectral denoising"):
  with members:
    - "nonlocal low rank tensor factor analysis for image restoration"
    - "taking human out of learning applications a survey on automated machine learning"
    - "weighted low rank tensor recovery for hyperspectral image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "robust low rank matrix factorization under general mixture noise distributions"

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)