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: "deep class aware denoising"
  Abstract: The increasing demand for high image quality in mobile devices brings forth the need for better computational enhancement techniques, and image denoising in particular. At the same time, the images captured by these devices can be categorized into a small set of semantic classes. However simple, this observation has not been exploited in image denoising until now. In this paper, we demonstrate how the reconstruction quality improves when a denoiser is aware of the type of content in the image. To this end, we first propose a new fully convolutional deep neural network architecture which is simple yet powerful as it achieves state-of-the-art performance even without being class-aware. We further show that a significant boost in performance of up to $0.4$ dB PSNR can be achieved by making our network class-aware, namely, by fine-tuning it for images belonging to a specific semantic class. Relying on the hugely successful existing image classifiers, this research advocates for using a class-aware approach in all image enhancement tasks.

Connected through hyperedge HE-1 (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 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"
    - "poisson inverse problems by the plug and play scheme"

Connected through hyperedge HE-2 (induced by source paper "xunit learning a spatial activation function for efficient image restoration"):
  with members:
    - "motion deblurring in the wild"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "enhanced deep residual networks for single image super resolution"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional denoising of low light images"
    - "an all in one network for dehazing and beyond"

Connected through hyperedge HE-3 (induced by source paper "classification driven dynamic image enhancement"):
  with members:
    - "clearing the skies a deep network architecture for single image rain removal"
    - "fast image processing with fully convolutional networks"
    - "examining the impact of blur on recognition by convolutional networks"
    - "an empirical study on the effects of different types of noise in image classification tasks"
    - "how image degradations affect deep cnn based face recognition"
    - "places an image database for deep scene understanding"
    - "does v nir based image enhancement come with better features"
    - "generating images with perceptual similarity metrics based on deep networks"

Connected through hyperedge HE-4 (induced by source paper "deep burst denoising"):
  with members:
    - "learning blind motion deblurring"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "comparative study of cnn and rnn for natural language processing"
    - "unsupervised monocular depth estimation with left right consistency"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "u net convolutional networks for biomedical image segmentation"

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)