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: "denoising hyperspectral image with non i i d noise structure"
  Abstract: Hyperspectral image (HSI) denoising has been attracting much research attention in remote sensing area due to its importance in improving the HSI qualities. The existing HSI denoising methods mainly focus on specific spectral and spatial prior knowledge in HSIs, and share a common underlying assumption that the embedded noise in HSI is independent and identically distributed (i.i.d.). In real scenarios, however, the noise existed in a natural HSI is always with much more complicated non-i.i.d. statistical structures and the under-estimation to this noise complexity often tends to evidently degenerate the robustness of current methods. To alleviate this issue, this paper attempts the first effort to model the HSI noise using a non-i.i.d. mixture of Gaussians (NMoG) noise assumption, which is finely in accordance with the noise characteristics possessed by a natural HSI and thus is capable of adapting various noise shapes encountered in real applications. Then we integrate such noise modeling strategy into the low-rank matrix factorization (LRMF) model and propose a NMoG-LRMF model in the Bayesian framework. A variational Bayes algorithm is designed to infer the posterior of the proposed model. All involved parameters can be recursively updated in closed-form. Compared with the current techniques, the proposed method performs more robust beyond the state-of-the-arts, as substantiated by our experiments implemented on synthetic and real noisy HSIs.

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"
    - "hyperspectral image denoising employing a spatial spectral deep residual convolutional neural network"
    - "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"
    - "memnet a persistent memory network for image restoration"
    - "quasi recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "color image and multispectral image denoising using block diagonal representation"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "renoir a dataset for real low light image noise reduction"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-3 (induced by source paper "hyperspectral image denoising employing a spatial spectral deep residual convolutional neural network"):
  with members:
    - "missing data reconstruction in remote sensing image with a unified spatial temporal spectral deep convolutional neural network"
    - "exploring models and data for remote sensing image caption generation"
    - "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"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "hybrid noise removal in hyperspectral imagery with a spatial spectral gradient network"):
  with members:
    - "hyperspectral image denoising employing a spatial spectral deep residual convolutional neural network"
    - "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"
    - "image restoration using convolutional auto encoders with symmetric skip connections"

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)