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: "multi level wavelet cnn for image restoration"
  Abstract: The tradeoff between receptive field size and efficiency is a crucial issue in low level vision. Plain convolutional networks (CNNs) generally enlarge the receptive field at the expense of computational cost. Recently, dilated filtering has been adopted to address this issue. But it suffers from gridding effect, and the resulting receptive field is only a sparse sampling of input image with checkerboard patterns. In this paper, we present a novel multi-level wavelet CNN (MWCNN) model for better tradeoff between receptive field size and computational efficiency. With the modified U-Net architecture, wavelet transform is introduced to reduce the size of feature maps in the contracting subnetwork. Furthermore, another convolutional layer is further used to decrease the channels of feature maps. In the expanding subnetwork, inverse wavelet transform is then deployed to reconstruct the high resolution feature maps. Our MWCNN can also be explained as the generalization of dilated filtering and subsampling, and can be applied to many image restoration tasks. The experimental results clearly show the effectiveness of MWCNN for image denoising, single image super-resolution, and JPEG image artifacts removal.

Connected through hyperedge HE-1 (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-2 (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"

Connected through hyperedge HE-3 (induced by source paper "comparative analysis of cnn based spatiotemporal reasoning in videos"):
  with members:
    - "resource efficient 3d convolutional neural networks"
    - "motion fused frames data level fusion strategy for hand gesture recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "a simple neural network module for relational reasoning"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "the something something video database for learning and evaluating visual common sense"
    - "temporal relational reasoning in videos"

Connected through hyperedge HE-4 (induced by source paper "fully hyperbolic convolutional neural networks"):
  with members:
    - "fast video object segmentation with spatio temporal gans"
    - "scale recurrent network for deep image deblurring"
    - "deep neural networks motivated by partial differential equations"
    - "switchable temporal propagation network"
    - "the reversible residual network backpropagation without storing activations"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "wavelet convolutional neural networks for texture classification"
    - "an end to end 3d convolutional neural network for action detection and segmentation in videos"

Connected through hyperedge HE-5 (induced by source paper "fastdvdnet towards real time video denoising without explicit motion estimation"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "non local video denoising by cnn"
    - "video object segmentation with language referring expressions"
    - "learning to see in the dark"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "deep high dynamic range imaging with large foreground motions"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "enhanced cnn for image denoising"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "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-7 (induced by source paper "multi path learnable wavelet neural network for image classification"):
  with members:
    - "deep learning in the wavelet domain"
    - "detailed dense inference with convolutional neural networks via discrete wavelet transform"
    - "wavelet based edge feature enhancement for convolutional neural networks"
    - "dynamic routing between capsules"
    - "scaling the scattering transform deep hybrid networks"
    - "scatternet hybrid deep learning shdl network for object classification"
    - "wavelet convolutional neural networks for texture classification"
    - "densely connected convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "difficulty aware image super resolution via deep adaptive dual network"):
  with members:
    - "cross modal attentional context learning for rgb d object detection"
    - "pirm challenge on perceptual image enhancement on smartphones report"
    - "fast and accurate single image super resolution via information distillation network"
    - "image super resolution using very deep residual channel attention networks"
    - "attention aware face hallucination via deep reinforcement learning"
    - "structure preserving image super resolution via contextualized multitask learning"
    - "enhanced deep residual networks for single image super resolution"
    - "memnet a persistent memory network for image restoration"

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)