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 networks for image super resolution with sparse prior"
  Abstract: Deep learning techniques have been successfully applied in many areas of computer vision, including low-level image restoration problems. For image super-resolution, several models based on deep neural networks have been recently proposed and attained superior performance that overshadows all previous handcrafted models. The question then arises whether large-capacity and data-driven models have become the dominant solution to the ill-posed super-resolution problem. In this paper, we argue that domain expertise represented by the conventional sparse coding model is still valuable, and it can be combined with the key ingredients of deep learning to achieve further improved results. We show that a sparse coding model particularly designed for super-resolution can be incarnated as a neural network, and trained in a cascaded structure from end to end. The interpretation of the network based on sparse coding leads to much more efficient and effective training, as well as a reduced model size. Our model is evaluated on a wide range of images, and shows clear advantage over existing state-of-the-art methods in terms of both restoration accuracy and human subjective quality.

Connected through hyperedge HE-1 (induced by source paper "human aware motion deblurring"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "gated fusion network for single image dehazing"
    - "scale recurrent network for deep image deblurring"
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "image correction via deep reciprocating hdr transformation"
    - "deep semantic face deblurring"
    - "multi channel weighted nuclear norm minimization for real color image denoising"

Connected through hyperedge HE-2 (induced by source paper "closed loop matters dual regression networks for single image super resolution"):
  with members:
    - "intelligent home 3d automatic 3d house design from linguistic descriptions only"
    - "hierarchical neural architecture search for single image super resolution"
    - "discrimination aware network pruning for deep model compression"
    - "multi marginal wasserstein gan"
    - "collaborative unsupervised domain adaptation for medical image diagnosis"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "ranksrgan generative adversarial networks with ranker for image super resolution"
    - "nat neural architecture transformer for accurate and compact architectures"

Connected through hyperedge HE-3 (induced by source paper "ada lista learned solvers adaptive to varying models"):
  with members:
    - "deep network classification by scattering and homotopy dictionary learning"
    - "deep energy using energy functions for unsupervised training of dnns"
    - "deep energy unsupervised training of deep neural networks"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "learning to learn by gradient descent by gradient descent"
    - "the little engine that could regularization by denoising red"

Connected through hyperedge HE-4 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions"):
  with members:
    - "scalable convolutional dictionary learning with constrained recurrent sparse auto encoders"
    - "handling incomplete heterogeneous data using vaes"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "simple efficient and neural algorithms for sparse coding"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"
    - "sparsity based poisson denoising with dictionary learning"

Connected through hyperedge HE-5 (induced by source paper "deep learning for single image super resolution a brief review"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "fast and accurate single image super resolution via information distillation network"
    - "super resolution based on image adapted cnn denoisers incorporating generalization of training data and internal learning in test time"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "the perception distortion tradeoff"
    - "super resolution via conditional implicit maximum likelihood estimation"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-6 (induced by source paper "a deep journey into super resolution a survey"):
  with members:
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "single image super resolution via cascaded multi scale cross network"
    - "fast and accurate single image super resolution via information distillation network"
    - "the relativistic discriminator a key element missing from standard gan"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "deep underwater image enhancement"
    - "image super resolution using very deep residual channel attention networks"

Connected through hyperedge HE-7 (induced by source paper "deep likelihood network for image restoration with multiple degradations"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "the perception distortion tradeoff"
    - "on the learning dynamics of deep neural networks"
    - "physics based generative adversarial models for image restoration and beyond"
    - "generative image inpainting with contextual attention"
    - "generative face completion"
    - "deep image prior"
    - "learning a single convolutional super resolution network for multiple degradations"

Connected through hyperedge HE-8 (induced by source paper "on low resolution face recognition in the wild comparisons and new techniques"):
  with members:
    - "generative adversarial networks an overview"
    - "additive margin softmax for face verification"
    - "deep semantic face deblurring"
    - "face super resolution through wasserstein gans"
    - "deep face deblurring"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "fsrnet end to end learning face super resolution with facial priors"

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)