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 image demosaicking using a cascade of convolutional residual denoising networks"
  Abstract: Demosaicking and denoising are among the most crucial steps of modern digital camera pipelines. Meanwhile, joint image denoising-demosaicking is a highly ill-posed inverse problem where at-least two-thirds of the information are missing and the rest are corrupted by noise. This poses a great challenge in obtaining meaningful reconstructions and a special care for the efficient treatment of the problem is required. While there are several machine learning approaches that have been recently introduced to solve this problem, in this work we propose a novel deep learning architecture which is inspired by powerful classical image regularization methods and large-scale convex optimization techniques. Consequently, our derived network is more transparent and has a clear interpretation compared to alternative competitive deep learning approaches. Our extensive experiments demonstrate that our network outperforms any previous approaches on both noisy and noise-free data. This improvement in reconstruction quality is attributed to the principled way we design our network architecture, which also requires fewer trainable parameters than the current state-of-the-art deep network solution. Finally, we show that our network has the ability to generalize well even when it is trained on small datasets, while keeping the overall number of parameters low.

Connected through hyperedge HE-1 (induced by source paper "neural imaging pipelines the scourge or hope of forensics"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "stereoscopic dark flash for low light photography"
    - "cnn based adversarial embedding for image steganography"
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "learning rich features for image manipulation detection"
    - "synthetic depth of field with a single camera mobile phone"
    - "do gans leave artificial fingerprints"
    - "content authentication for neural imaging pipelines end to end optimization of photo provenance in complex distribution channels"

Connected through hyperedge HE-2 (induced by source paper "learning digital camera pipeline for extreme low light imaging"):
  with members:
    - "image super resolution using very deep residual channel attention networks"
    - "toward convolutional blind denoising of real photographs"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "deep exemplar based colorization"
    - "learning to see in the dark"
    - "pieapp perceptual image error assessment through pairwise preference"
    - "learned perceptual image enhancement"
    - "deep bilateral learning for real time image enhancement"

Connected through hyperedge HE-3 (induced by source paper "pixel adaptive filtering units"):
  with members:
    - "pixel adaptive convolutional neural networks"
    - "iterative joint image demosaicking and denoising using a residual denoising network"
    - "neural nearest neighbors networks"
    - "segmentation aware convolutional networks using local attention masks"
    - "blade filter learning for general purpose computational photography"
    - "convolutional networks with adaptive inference graphs"
    - "enhanced deep residual networks for single image super resolution"
    - "burst denoising with kernel prediction networks"

Connected through hyperedge HE-4 (induced by source paper "joint demosaicking and denoising by fine tuning of bursts of raw images"):
  with members:
    - "learning to adapt for stereo"
    - "noise2self blind denoising by self supervision"
    - "real time self adaptive deep stereo"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"
    - "noise2noise learning image restoration without clean data"
    - "learning to see in the dark"

Connected through hyperedge HE-5 (induced by source paper "enhanced cnn for image denoising"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "multi level wavelet cnn for image restoration"
    - "deep learning for image denoising a survey"
    - "a cascaded convolutional neural network for x ray low dose ct image denoising"
    - "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"
    - "block matching convolutional neural network for image denoising"

Connected through hyperedge HE-6 (induced by source paper "iterative joint image demosaicking and denoising using a residual denoising network"):
  with members:
    - "on the convergence of adam and beyond"
    - "denoising prior driven deep neural network for image restoration"
    - "deep residual network for joint demosaicing and super resolution"
    - "learning deep cnn denoiser prior for image restoration"
    - "ista net iterative shrinkage thresholding algorithm inspired deep network for image compressive sensing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "universal denoising networks a novel cnn architecture for image denoising"
    - "inexact proximal gradient methods for non convex and non smooth optimization"

Connected through hyperedge HE-7 (induced by source paper "cross channel correlation preserved three stream lightweight cnns for demosaicking"):
  with members:
    - "low cost edge sensing for high quality demosaicking"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-8 (induced by source paper "content authentication for neural imaging pipelines end to end optimization of photo provenance in complex distribution channels"):
  with members:
    - "learning rich features for image manipulation detection"
    - "noise2noise learning image restoration without clean data"
    - "noiseprint a cnn based camera model fingerprint"
    - "detection of deep network generated images using disparities in color components"
    - "learning to see in the dark"
    - "learning deep convolutional networks for demosaicing"
    - "wespe weakly supervised photo enhancer for digital cameras"
    - "hdr image reconstruction from a single exposure using deep cnns"

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)