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: "self tuned deep super resolution"
  Abstract: Deep learning has been successfully applied to image super resolution (SR). In this paper, we propose a deep joint super resolution (DJSR) model to exploit both external and self similarities for SR. A Stacked Denoising Convolutional Auto Encoder (SDCAE) is first pre-trained on external examples with proper data augmentations. It is then fine-tuned with multi-scale self examples from each input, where the reliability of self examples is explicitly taken into account. We also enhance the model performance by sub-model training and selection. The DJSR model is extensively evaluated and compared with state-of-the-arts, and show noticeable performance improvements both quantitatively and perceptually on a wide range of images.

Connected through hyperedge HE-1 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "enlightengan deep light enhancement without paired supervision"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "joint enhancement and denoising method via sequential decomposition"
    - "getting to know low light images with the exclusively dark dataset"
    - "yolov3 an incremental improvement"
    - "pyramidbox a context assisted single shot face detector"

Connected through hyperedge HE-2 (induced by source paper "super resolution via image adapted denoising cnns incorporating external and internal learning"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "class aware fully convolutional gaussian and poisson denoising"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "regularization by denoising clarifications and new interpretations"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "a fully progressive approach to single image super resolution"

Connected through hyperedge HE-3 (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-4 (induced by source paper "multimedia super resolution via deep learning a survey"):
  with members:
    - "joint convolutional neural pyramid for depth map super resolution"
    - "learning a mixture of deep networks for single image super resolution"
    - "an analysis of deep neural network models for practical applications"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "failures of gradient based deep learning"
    - "enhanced deep residual networks for single image super resolution"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "single image super resolution when model adaptation matters"

Connected through hyperedge HE-5 (induced by source paper "image super resolution via dual state recurrent networks"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "dual path networks"
    - "sharing residual units through collective tensor factorization in deep neural networks"
    - "accelerating the super resolution convolutional neural network"
    - "beyond deep residual learning for image restoration persistent homology guided manifold simplification"
    - "deep networks with stochastic depth"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "densely connected convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "advanced super resolution using lossless pooling convolutional networks"):
  with members:
    - "detail revealing deep video super resolution"
    - "end to end learning of video super resolution with motion compensation"
    - "accelerating the super resolution convolutional neural network"
    - "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"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "real time video super resolution with spatio temporal networks and motion compensation"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "orthogonally regularized deep networks for image super resolution"):
  with members:
    - "learning deconvolution network for semantic segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "deep image super resolution via natural image priors"):
  with members:
    - "sparsity based color image super resolution via exploiting cross channel constraints"
    - "deep residual learning for image recognition"

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.LG(Machine Learning)