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: "learning deep convolutional networks for demosaicing"
  Abstract: This paper presents a comprehensive study of applying the convolutional neural network (CNN) to solving the demosaicing problem. The paper presents two CNN models that learn end-to-end mappings between the mosaic samples and the original image patches with full information. In the case the Bayer color filter array (CFA) is used, an evaluation with ten competitive methods on popular benchmarks confirms that the data-driven, automatically learned features by the CNN models are very effective. Experiments show that the proposed CNN models can perform equally well in both the sRGB space and the linear space. It is also demonstrated that the CNN model can perform joint denoising and demosaicing. The CNN model is very flexible and can be easily adopted for demosaicing with any CFA design. We train CNN models for demosaicing with three different CFAs and obtain better results than existing methods. With the great flexibility to be coupled with any CFA, we present the first data-driven joint optimization of the CFA design and the demosaicing method using CNN. Experiments show that the combination of the automatically discovered CFA pattern and the automatically devised demosaicing method significantly outperforms the current best demosaicing results. Visual comparisons confirm that the proposed methods reduce more visual artifacts than existing methods. Finally, we show that the CNN model is also effective for the more general demosaicing problem with spatially varying exposure and color and can be used for taking images of higher dynamic ranges with a single shot. The proposed models and the thorough experiments together demonstrate that CNN is an effective and versatile tool for solving the demosaicing problem.

Connected through hyperedge HE-1 (induced by source paper "replacing mobile camera isp with a single deep learning model"):
  with members:
    - "ai benchmark all about deep learning on smartphones in 2019"
    - "aim 2019 challenge on image demoireing dataset and study"
    - "semantic image synthesis with spatially adaptive normalization"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "nice noise injection and clamping estimation for neural network quantization"
    - "ai benchmark running deep neural networks on android smartphones"
    - "fast perceptual image enhancement"

Connected through hyperedge HE-2 (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"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"

Connected through hyperedge HE-3 (induced by source paper "joint demosaicing and denoising by overfitting of bursts of raw images"):
  with members:
    - "handheld multi frame super resolution"
    - "learning to adapt for stereo"
    - "noise2self blind denoising by self supervision"
    - "real time self adaptive deep stereo"
    - "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-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"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "noise2noise learning image restoration without clean data"

Connected through hyperedge HE-5 (induced by source paper "deep demosaicing for edge implementation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "efficient neural architecture search via parameter sharing"
    - "learning transferable architectures for scalable image recognition"
    - "sgdr stochastic gradient descent with restarts"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-6 (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"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "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"
    - "wespe weakly supervised photo enhancer for digital cameras"
    - "hdr image reconstruction from a single exposure using deep cnns"

Connected through hyperedge HE-7 (induced by source paper "see far with tpnet a tile processor and a cnn symbiosis"):
  with members:
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"
    - "structure from motion using dense cnn features with keypoint relocalization"
    - "the sound of pixels"
    - "learning to see in the dark"
    - "learning fully convolutional networks for iterative non blind deconvolution"
    - "computing the stereo matching cost with a convolutional neural network"
    - "fast convolutional nets with fbfft a gpu performance evaluation"

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)