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: "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
  Abstract: Image restoration is a long-standing problem in low-level computer vision with many interesting applications. We describe a flexible learning framework based on the concept of nonlinear reaction diffusion models for various image restoration problems. By embodying recent improvements in nonlinear diffusion models, we propose a dynamic nonlinear reaction diffusion model with time-dependent parameters ( i.e. , linear filters and influence functions). In contrast to previous nonlinear diffusion models, all the parameters, including the filters and the influence functions, are simultaneously learned from training data through a loss based approach. We call this approach TNRD— Trainable Nonlinear Reaction Diffusion . The TNRD approach is applicable for a variety of image restoration tasks by incorporating appropriate reaction force. We demonstrate its capabilities with three representative applications, Gaussian image denoising, single image super resolution and JPEG deblocking. Experiments show that our trained nonlinear diffusion models largely benefit from the training of the parameters and finally lead to the best reported performance on common test datasets for the tested applications. Our trained models preserve the structural simplicity of diffusion models and take only a small number of diffusion steps, thus are highly efficient. Moreover, they are also well-suited for parallel computation on GPUs, which makes the inference procedure extremely fast.

Connected through hyperedge HE-1 (induced by source paper "transfer learning from synthetic to real noise denoising with adaptive instance normalization"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "noise flow noise modeling with conditional normalizing flows"
    - "real image denoising with feature attention"
    - "semantic image synthesis with spatially adaptive normalization"
    - "suppressing model overfitting for image super resolution networks"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "image super resolution using very deep residual channel attention networks"

Connected through hyperedge HE-2 (induced by source paper "stochastic frequency masking to improve super resolution and denoising networks"):
  with members:
    - "blind super resolution with iterative kernel correction"
    - "noise2self blind denoising by self supervision"
    - "real image denoising with feature attention"
    - "zoom to learn learn to zoom"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "learning deep gradient descent optimization for image deconvolution"):
  with members:
    - "mptv matching pursuit based total variation minimization for image deconvolution"
    - "online video deblurring via dynamic temporal blending network"
    - "learning blind motion deblurring"
    - "learning deep cnn denoiser prior for image restoration"
    - "learning aggregated transmission propagation networks for haze removal and beyond"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "learning to learn by gradient descent by gradient descent"
    - "learning fully convolutional networks for iterative non blind deconvolution"

Connected through hyperedge HE-4 (induced by source paper "deep learning based video coding a review and a case study"):
  with members:
    - "learning for video compression"
    - "image and video compression with neural networks a review"
    - "enhancing quality for hevc compressed videos"
    - "on the classification distortion perception tradeoff"
    - "learning based facial image compression with semantic fidelity metric"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "learned scalable image compression with bidirectional context disentanglement network"
    - "enlarging context with low cost efficient arithmetic coding with trimmed convolution"

Connected through hyperedge HE-5 (induced by source paper "3d packing for self supervised monocular depth estimation"):
  with members:
    - "two stream networks for self supervised ego motion estimation"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "revisiting self supervised visual representation learning"
    - "group normalization"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "hartley spectral pooling for deep learning"

Connected through hyperedge HE-6 (induced by source paper "multi level wavelet convolutional neural networks"):
  with members:
    - "parallel grid pooling for data augmentation"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "image super resolution using very deep residual channel attention networks"
    - "toward convolutional blind denoising of real photographs"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "learning deep cnn denoiser prior for image restoration"

Connected through hyperedge HE-7 (induced by source paper "index network"):
  with members:
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "generating training data for denoising real rgb images via camera pipeline simulation"
    - "deeperlab single shot image parser"
    - "indices matter learning to index for deep image matting"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "learning to see in the dark"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-8 (induced by source paper "cfsnet toward a controllable feature space for image restoration"):
  with members:
    - "lightweight feature fusion network for single image super resolution"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "maintaining natural image statistics with the contextual loss"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "toward convolutional blind denoising of real photographs"
    - "the perception distortion tradeoff"

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)