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: "learning super resolution jointly from external and internal examples"
  Abstract: Single image super-resolution (SR) aims to estimate a high-resolution (HR) image from a low-resolution (LR) input. Image priors are commonly learned to regularize the, otherwise, seriously ill-posed SR problem, either using external LR–HR pairs or internal similar patterns. We propose joint SR to adaptively combine the advantages of both external and internal SR methods. We define two loss functions using sparse coding-based external examples, and epitomic matching based on internal examples, as well as a corresponding adaptive weight to automatically balance their contributions according to their reconstruction errors. Extensive SR results demonstrate the effectiveness of the proposed method over the existing state-of-the-art methods, and is also verified by our subjective evaluation study.

Connected through hyperedge HE-1 (induced by source paper "meta transfer learning for zero shot super resolution"):
  with members:
    - "blind super resolution with iterative kernel correction"
    - "singan learning a generative model from a single natural image"
    - "residual non local attention networks for image restoration"
    - "feedback network for image super resolution"
    - "on first order meta learning algorithms"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-2 (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-3 (induced by source paper "u finger multi scale dilated convolutional network for fingerprint image denoising and inpainting"):
  with members:
    - "enhance visual recognition under adverse conditions via deep networks"
    - "improved techniques for learning to dehaze and beyond a collective study"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "robust minutiae extractor integrating deep networks and fingerprint domain knowledge"
    - "fingernet an unified deep network for fingerprint minutiae extraction"
    - "studying very low resolution recognition using deep networks"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-4 (induced by source paper "the power of complementary regularizers image recovery via transform learning and low rank modeling"):
  with members:
    - "non local recurrent network for image restoration"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "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"
    - "beyond deep residual learning for image restoration persistent homology guided manifold simplification"
    - "image denoising via group sparsity residual constraint"
    - "frist flipping and rotation invariant sparsifying transform learning and applications"

Connected through hyperedge HE-5 (induced by source paper "benchmarking single image dehazing and beyond"):
  with members:
    - "improved techniques for learning to dehaze and beyond a collective study"
    - "semantic foggy scene understanding with synthetic data"
    - "end to end united video dehazing and detection"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "robust emotion recognition from low quality and low bit rate video a deep learning approach"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-6 (induced by source paper "enhanced deep residual networks for single image super resolution"):
  with members:
    - "accelerating the super resolution convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "seven ways to improve example based single image super resolution"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "learning a mixture of deep networks for single image super resolution"):
  with members:
    - "studying very low resolution recognition using deep networks"
    - "seven ways to improve example based single image super resolution"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep networks for image super resolution with sparse prior"
    - "image super resolution using deep convolutional networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "robust emotion recognition from low quality and low bit rate video a deep learning approach"):
  with members:
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "how deep neural networks can improve emotion recognition on video data"
    - "studying very low resolution recognition using deep networks"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "self tuned deep super resolution"

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)