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: "poisson noise reduction with non local pca"
  Abstract: Photon-limited imaging arises when the number of photons collected by a sensor array is small relative to the number of detector elements. Photon limitations are an important concern for many applications such as spectral imaging, night vision, nuclear medicine, and astronomy. Typically a Poisson distribution is used to model these observations, and the inherent heteroscedasticity of the data combined with standard noise removal methods yields significant artifacts. This paper introduces a novel denoising algorithm for photon-limited images which combines elements of dictionary learning and sparse patch-based representations of images. The method employs both an adaptation of Principal Component Analysis (PCA) for Poisson noise and recently developed sparsity-regularized convex optimization algorithms for photon-limited images. A comprehensive empirical evaluation of the proposed method helps characterize the performance of this approach relative to other state-of-the-art denoising methods. The results reveal that, despite its conceptual simplicity, Poisson PCA-based denoising appears to be highly competitive in very low light regimes.

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 "deeplsr a deep learning approach for laser speckle reduction"):
  with members:
    - "deep learning with cinematic rendering fine tuning deep neural networks using photorealistic medical images"
    - "spectral normalization for generative adversarial networks"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "steerable e pca rotationally invariant exponential family pca"):
  with members:
    - "steerable principal components for space frequency localized images"
    - "fast steerable principal component analysis"

Connected through hyperedge HE-5 (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-6 (induced by source paper "external patch based image restoration using importance sampling"):
  with members:
    - "poisson image denoising using best linear prediction a post processing framework"
    - "class specific image denoising using importance sampling"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep class aware denoising"
    - "deep convolutional denoising of low light images"
    - "image restoration and reconstruction using variable splitting and class adapted image priors"
    - "deep gaussian conditional random field network a model based deep network for discriminative denoising"

Connected through hyperedge HE-7 (induced by source paper "external prior guided internal prior learning for real world noisy image denoising"):
  with members:
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "benchmarking denoising algorithms with real photographs"
    - "adaptive image denoising by targeted databases"
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "removing mixture of gaussian and impulse noise by patch based weighted means"

Connected through hyperedge HE-8 (induced by source paper "poisson image denoising using best linear prediction a post processing framework"):
  with members:
    - "class specific poisson denoising by patch based importance sampling"
    - "poisson inverse problems by the plug and play scheme"
    - "mmse estimation for poisson noise removal in images"
    - "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)