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: "blind super resolution with iterative kernel correction"
  Abstract: Deep learning based methods have dominated super-resolution (SR) field due to their remarkable performance in terms of effectiveness and efficiency. Most of these methods assume that the blur kernel during downsampling is predefined/known (e.g., bicubic). However, the blur kernels involved in real applications are complicated and unknown, resulting in severe performance drop for the advanced SR methods. In this paper, we propose an Iterative Kernel Correction (IKC) method for blur kernel estimation in blind SR problem, where the blur kernels are unknown. We draw the observation that kernel mismatch could bring regular artifacts (either over-sharpening or over-smoothing), which can be applied to correct inaccurate blur kernels. Thus we introduce an iterative correction scheme -- IKC that achieves better results than direct kernel estimation. We further propose an effective SR network architecture using spatial feature transform (SFT) layers to handle multiple blur kernels, named SFTMD. Extensive experiments on synthetic and real-world images show that the proposed IKC method with SFTMD can provide visually favorable SR results and the state-of-the-art performance in blind SR problem.

Connected through hyperedge HE-1 (induced by source paper "meta transfer learning for zero shot super resolution"):
  with members:
    - "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"
    - "image super resolution using very deep residual channel attention networks"

Connected through hyperedge HE-2 (induced by source paper "deep blind video super resolution"):
  with members:
    - "blind super resolution kernel estimation using an internal gan"
    - "generative adversarial networks and perceptual losses for video super resolution"
    - "recurrent back projection network for video super resolution"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "video enhancement with task oriented flow"
    - "neural blind deconvolution using deep priors"
    - "frame recurrent video super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"

Connected through hyperedge HE-3 (induced by source paper "stochastic frequency masking to improve super resolution and denoising networks"):
  with members:
    - "noise2self blind denoising by self supervision"
    - "real image denoising with feature attention"
    - "zoom to learn learn to zoom"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "noise2void learning denoising from single noisy images"

Connected through hyperedge HE-4 (induced by source paper "blind image restoration without prior knowledge"):
  with members:
    - "blind universal bayesian image denoising with gaussian noise level learning"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "deep network interpolation for continuous imagery effect transition"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-5 (induced by source paper "fast adaptation to super resolution networks via meta learning"):
  with members:
    - "on first order meta learning algorithms"
    - "fast and accurate single image super resolution via information distillation network"
    - "image super resolution using very deep residual channel attention networks"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "meta reinforcement learning of structured exploration strategies"
    - "meta transfer learning for few shot learning"
    - "task agnostic meta learning for few shot learning"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-6 (induced by source paper "suppressing model overfitting for image super resolution networks"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "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"
    - "investigating backtranslation in neural machine translation"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "data augmentation by pairing samples for images classification"
    - "image super resolution using very deep residual channel attention networks"

Connected through hyperedge HE-7 (induced by source paper "ranksrgan generative adversarial networks with ranker for image super resolution"):
  with members:
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "suppressing model overfitting for image super resolution networks"
    - "deep neural networks for no reference and full reference image quality assessment"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the perception distortion tradeoff"
    - "ranking cgans subjective control over semantic image attributes"

Connected through hyperedge HE-8 (induced by source paper "blind super resolution kernel estimation using an internal gan"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "image super resolution using very deep residual channel attention networks"
    - "ingan capturing and remapping the dna of a natural image"
    - "spectral normalization for generative adversarial networks"
    - "wide activation for efficient and accurate image super resolution"
    - "a fully progressive approach to single image super resolution"
    - "learning a single convolutional super resolution network for multiple degradations"

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)