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: "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"
  Abstract: As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation owes to the development of the l1-norm optimization techniques and the fact that natural images are intrinsically sparse in some domains. The image restoration quality largely depends on whether the employed sparse domain can represent well the underlying image. Considering that the contents can vary significantly across different images or different patches in a single image, we propose to learn various sets of bases from a precollected dataset of example image patches, and then, for a given patch to be processed, one set of bases are adaptively selected to characterize the local sparse domain. We further introduce two adaptive regularization terms into the sparse representation framework. First, a set of autoregressive (AR) models are learned from the dataset of example image patches. The best fitted AR models to a given patch are adaptively selected to regularize the image local structures. Second, the image nonlocal self-similarity is introduced as another regularization term. In addition, the sparsity regularization parameter is adaptively estimated for better image restoration performance. Extensive experiments on image deblurring and super-resolution validate that by using adaptive sparse domain selection and adaptive regularization, the proposed method achieves much better results than many state-of-the-art algorithms in terms of both PSNR and visual perception.

Connected through hyperedge HE-1 (induced by source paper "improving robustness of deep learning based image reconstruction"):
  with members:
    - "a direct approach to robust deep learning using adversarial networks"
    - "gan based projector for faster recovery with convergence guarantees in linear inverse problems"
    - "evaluating robustness of deep image super resolution against adversarial attacks"
    - "on instabilities of deep learning in image reconstruction does ai come at a cost"
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "scaling provable adversarial defenses"
    - "adversarially robust generalization requires more data"
    - "generating adversarial examples with adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "srobb targeted perceptual loss for single image super resolution"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "the unreasonable effectiveness of texture transfer for single image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-3 (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-4 (induced by source paper "toward real world single image super resolution a new benchmark and a new model"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "zoom to learn learn to zoom"
    - "camera lens super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "toward bridging the simulated to real gap benchmarking super resolution on real data"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-5 (induced by source paper "adaptive transform domain image super resolution via orthogonally regularized deep networks"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "orthogonally regularized deep networks for image super resolution"
    - "residual dense network for image super resolution"
    - "deep image super resolution via natural image priors"
    - "a frequency domain neural network for fast image super resolution"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "sparsity based color image super resolution via exploiting cross channel constraints"

Connected through hyperedge HE-6 (induced by source paper "identity aware deep face hallucination via adversarial face verification"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "sigan siamese generative adversarial network for identity preserving face hallucination"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "style and content disentanglement in generative adversarial networks"
    - "multibiometric secure system based on deep learning"
    - "attention aware face hallucination via deep reinforcement learning"
    - "biometrics as a service a framework to promote innovative biometric recognition in the cloud"

Connected through hyperedge HE-7 (induced by source paper "generative collaborative networks for single image super resolution"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "learning more universal representations for transfer learning"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "perceptual losses for real time style transfer and super resolution"
    - "accelerating the super resolution convolutional neural network"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "amortised map inference for image super resolution"

Connected through hyperedge HE-8 (induced by source paper "fh gan face hallucination and recognition using generative adversarial network"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "super identity convolutional neural network for face hallucination"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "improved training of wasserstein gans"
    - "memnet a persistent memory network for image restoration"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"

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)