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: "sparsity based poisson denoising with dictionary learning"
  Abstract: The problem of Poisson denoising appears in various imaging applications, such as low-light photography, medical imaging, and microscopy. In cases of high SNR, several transformations exist so as to convert the Poisson noise into an additive-independent identically distributed. Gaussian noise, for which many effective algorithms are available. However, in a low-SNR regime, these transformations are significantly less accurate, and a strategy that relies directly on the true noise statistics is required. Salmon et al. took this route, proposing a patch-based exponential image representation model based on Gaussian mixture model, leading to state-of-the-art results. In this paper, we propose to harness sparse-representation modeling to the image patches, adopting the same exponential idea. Our scheme uses a greedy pursuit with boot-strapping-based stopping condition and dictionary learning within the denoising process. The reconstruction performance of the proposed scheme is competitive with leading methods in high SNR and achieving state-of-the-art results in cases of low SNR.

Connected through hyperedge HE-1 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions"):
  with members:
    - "scalable convolutional dictionary learning with constrained recurrent sparse auto encoders"
    - "handling incomplete heterogeneous data using vaes"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "simple efficient and neural algorithms for sparse coding"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "deep networks for image super resolution with sparse prior"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "deepred deep image prior powered by red"):
  with members:
    - "a bayesian perspective on the deep image prior"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "image super resolution using very deep residual channel attention networks"
    - "neural nearest neighbors networks"
    - "non local recurrent network for image restoration"
    - "image restoration using total variation regularized deep image prior"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "deep image prior"

Connected through hyperedge HE-3 (induced by source paper "class aware fully convolutional gaussian and poisson denoising"):
  with members:
    - "class specific image denoising using importance sampling"
    - "image restoration by iterative denoising and backward projections"
    - "deep burst denoising"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "blind image deblurring using class adapted image priors"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "benchmarking denoising algorithms with real photographs"

Connected through hyperedge HE-4 (induced by source paper "image reconstruction using deep learning"):
  with members:
    - "deep convolutional denoising of low light images"
    - "medical image denoising using convolutional denoising autoencoders"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "llnet a deep autoencoder approach to natural low light image enhancement"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"):
  with members:
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "insights into analysis operator learning from patch based sparse models to higher order mrfs"
    - "learning activation functions to improve deep neural networks"
    - "revisiting loss specific training of filter based mrfs for image restoration"

Connected through hyperedge HE-6 (induced by source paper "class specific poisson denoising by patch based importance sampling"):
  with members:
    - "image restoration and reconstruction using variable splitting and class adapted image priors"
    - "poisson inverse problems by the plug and play scheme"
    - "adaptive image denoising by targeted databases"
    - "poisson noise reduction with non local pca"

Connected through hyperedge HE-7 (induced by source paper "patch ordering as a regularization for inverse problems in image processing"):
  with members:
    - "boosting of image denoising algorithms"
    - "image processing using smooth ordering of its patches"
    - "redundant wavelets on graphs and high dimensional data clouds"
    - "poisson noise reduction with non local pca"
    - "generalized tree based wavelet transform"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-8 (induced by source paper "poisson inverse problems by the plug and play scheme"):
  with members:
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "poisson noise reduction with non local pca"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

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)