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: "dilated deep residual network for image denoising"
  Abstract: Variations of deep neural networks such as convolutional neural network (CNN) have been successfully applied to image denoising. The goal is to automatically learn a mapping from a noisy image to a clean image given training data consisting of pairs of noisy and clean images. Most existing CNN models for image denoising have many layers. In such cases, the models involve a large amount of parameters and are computationally expensive to train. In this paper, we develop a dilated residual CNN for Gaussian image denoising. Compared with the recently proposed residual denoiser, our method can achieve comparable performance with less computational cost. Specifically, we enlarge receptive field by adopting dilated convolution in residual network, and the dilation factor is set to a certain value. We utilize appropriate zero padding to make the dimension of the output the same as the input. It has been proven that the expansion of receptive field can boost the CNN performance in image classification, and we further demonstrate that it can also lead to competitive performance for denoising problem. Moreover, we present a formula to calculate receptive field size when dilated convolution is incorporated. Thus, the change of receptive field can be interpreted mathematically. To validate the efficacy of our approach, we conduct extensive experiments for both gray and color image denoising with specific or randomized noise levels. Both of the quantitative measurements and the visual results of denoising are promising comparing with state-of-the-art baselines.

Connected through hyperedge HE-1 (induced by source paper "near lossless ell_ infty constrained image decompression via deep neural network"):
  with members:
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "generative adversarial networks for extreme learned image compression"
    - "conditional probability models for deep image compression"
    - "deep back projection networks for super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "deep generative adversarial compression artifact removal"
    - "single image super resolution with dilated convolution based multi scale information learning inception module"
    - "learning convolutional networks for content weighted image compression"

Connected through hyperedge HE-2 (induced by source paper "deep learning for image denoising a survey"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "group normalization"
    - "a variational u net for conditional appearance and shape generation"
    - "residual dense network for image super resolution"
    - "correction by projection denoising images with generative adversarial networks"
    - "learning a single convolutional super resolution network for multiple degradations"

Connected through hyperedge HE-3 (induced by source paper "learning long range spatial dependencies with horizontal gated recurrent units"):
  with members:
    - "non local neural networks"
    - "do deep neural networks suffer from crowding"
    - "richer convolutional features for edge detection"
    - "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"
    - "identity mappings in deep residual networks"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "pixel recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "a multiscale image denoising algorithm based on dilated residual convolution network"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "dilated residual networks"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "understanding convolution for semantic segmentation"
    - "beyond deep residual learning for image restoration persistent homology guided manifold simplification"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-5 (induced by source paper "imbalanced malware images classification a cnn based approach"):
  with members:
    - "an elu network with total variation for image denoising"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "deepedge a multi scale bifurcated deep network for top down contour detection"
    - "matconvnet convolutional neural networks for matlab"

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)