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: "zoom to learn learn to zoom"
  Abstract: This paper shows that when applying machine learning to digital zoom for photography, it is beneficial to use real, RAW sensor data for training. Existing learning-based super-resolution methods do not use real sensor data, instead operating on RGB images. In practice, these approaches result in loss of detail and accuracy in their digitally zoomed output when zooming in on distant image regions. We also show that synthesizing sensor data by resampling high-resolution RGB images is an oversimplified approximation of real sensor data and noise, resulting in worse image quality. The key barrier to using real sensor data for training is that ground truth high-resolution imagery is missing. We show how to obtain the ground-truth data with optically zoomed images and contribute a dataset, SR-RAW, for real-world computational zoom. We use SR-RAW to train a deep network with a novel contextual bilateral loss (CoBi) that delivers critical robustness to mild misalignment in input-output image pairs. The trained network achieves state-of-the-art performance in 4X and 8X computational zoom.

Connected through hyperedge HE-1 (induced by source paper "perceptual image super resolution with progressive adversarial network"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "analyzing and improving the image quality of stylegan"
    - "deep learning for image super resolution a survey"
    - "camera lens super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "the 2018 pirm challenge on perceptual image super resolution"

Connected through hyperedge HE-2 (induced by source paper "stochastic frequency masking to improve super resolution and denoising networks"):
  with members:
    - "blind super resolution with iterative kernel correction"
    - "noise2self blind denoising by self supervision"
    - "real image denoising with feature attention"
    - "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-3 (induced by source paper "image restoration for under display camera"):
  with members:
    - "when awgn based denoiser meets real noises"
    - "dense haze a benchmark for image dehazing with dense haze and haze free images"
    - "noise flow noise modeling with conditional normalizing flows"
    - "learning raw image denoising with bayer pattern unification and bayer preserving augmentation"
    - "camera lens super resolution"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "learning to synthesize motion blur"
    - "esrgan enhanced super resolution generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "domain specific image super resolution with progressive adversarial network"):
  with members:
    - "camera lens super resolution"
    - "adversarial noise layer regularize neural network by adding noise"
    - "high resolution mammogram synthesis using progressive generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-5 (induced by source paper "learning to have an ear for face super resolution"):
  with members:
    - "seeing what a gan cannot generate"
    - "what makes training multi modal networks hard"
    - "toward real world single image super resolution a new benchmark and a new model"
    - "two stream action recognition oriented video super resolution"
    - "image super resolution by neural texture transfer"
    - "ranksrgan generative adversarial networks with ranker for image super resolution"
    - "evaluating robustness of deep image super resolution against adversarial attacks"
    - "speech2face learning the face behind a voice"

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"
    - "blind super resolution with iterative kernel correction"
    - "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 "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"
    - "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"
    - "learning a single convolutional super resolution network for multiple degradations"

Connected through hyperedge HE-8 (induced by source paper "component attention guided face super resolution network cagface"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "feedback network for image super resolution"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "the relativistic discriminator a key element missing from standard gan"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimedia super resolution via deep learning a survey"
    - "a style based generator architecture for generative adversarial networks"

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)