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: "seven ways to improve example based single image super resolution"
  Abstract: In this paper we present seven techniques that everybody should know to improve example-based single image super resolution (SR): 1) augmentation of data, 2) use of large dictionaries with efficient search structures, 3) cascading, 4) image self-similarities, 5) back projection refinement, 6) enhanced prediction by consistency check, and 7) context reasoning. We validate our seven techniques on standard SR benchmarks (i.e. Set5, Set14, B100) and methods (i.e. A+, SRCNN, ANR, Zeyde, Yang) and achieve substantial improvements.The techniques are widely applicable and require no changes or only minor adjustments of the SR methods. Moreover, our Improved A+ (IA) method sets new state-of-the-art results outperforming A+ by up to 0.9dB on average PSNR whilst maintaining a low time complexity.

Connected through hyperedge HE-1 (induced by source paper "transfer learning from synthetic to real noise denoising with adaptive instance normalization"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "noise flow noise modeling with conditional normalizing flows"
    - "real image denoising with feature attention"
    - "semantic image synthesis with spatially adaptive normalization"
    - "suppressing model overfitting for image super resolution networks"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "image super resolution using very deep residual channel attention networks"

Connected through hyperedge HE-2 (induced by source paper "deep learning for single image super resolution a brief review"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "fast and accurate single image super resolution via information distillation network"
    - "super resolution based on image adapted cnn denoisers incorporating generalization of training data and internal learning in test time"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "the perception distortion tradeoff"
    - "super resolution via conditional implicit maximum likelihood estimation"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-3 (induced by source paper "deep back projection networks for single image super resolution"):
  with members:
    - "frame recurrent video super resolution"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "sketch based manga retrieval using manga109 dataset"
    - "enhanced deep residual networks for single image super resolution"
    - "detail revealing deep video super resolution"

Connected through hyperedge HE-4 (induced by source paper "hran hybrid residual attention network for single image super resolution"):
  with members:
    - "lightweight image super resolution with adaptive weighted learning network"
    - "distilling with residual network for single image super resolution"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "image super resolution using very deep residual channel attention networks"
    - "feature pyramid network for multi class land segmentation"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"
    - "big little net an efficient multi scale feature representation for visual and speech recognition"

Connected through hyperedge HE-5 (induced by source paper "lightweight image super resolution with adaptive weighted learning network"):
  with members:
    - "fast accurate and lightweight super resolution with neural architecture search"
    - "multi objective reinforced evolution in mobile neural architecture search"
    - "deep learning for image super resolution a survey"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "residual convolutional neural network revisited with active weighted mapping"
    - "image super resolution using very deep residual channel attention networks"
    - "wide activation for efficient and accurate image super resolution"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-6 (induced by source paper "feedback network for image super resolution"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "top down feedback for crowd counting convolutional neural network"
    - "a fully progressive approach to single image super resolution"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"
    - "image super resolution via dual state recurrent networks"

Connected through hyperedge HE-7 (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-8 (induced by source paper "edvr video restoration with enhanced deformable convolutional networks"):
  with members:
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "adversarial spatio temporal learning for video deblurring"
    - "path restore learning network path selection for image restoration"
    - "video enhancement with task oriented flow"
    - "frame recurrent video super resolution"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "object detection in video with spatiotemporal sampling networks"
    - "esrgan enhanced super resolution 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)