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: "structure preserving image super resolution via contextualized multitask learning"
  Abstract: Single-image super-resolution (SR), which refers to reconstructing a higher resolution image from the observed low-resolution (LR) image, has received substantial attention due to its tremendous application potentials. Despite the breakthroughs of recently proposed SR methods using convolutional neural networks, their generated results usually lack of preserving structural (high-frequency) details. In this paper, regarding global boundary context and residual context as complimentary information for enhancing structural details in image restoration, we develop a contextualized multitask learning framework to address the SR problem. Specifically, our method first extracts convolutional features from the input LR image and applies one deconvolutional module to interpolate the LR feature maps in a content-adaptive way. Then, the resulting feature maps are fed into two branched subnetworks. On several standard benchmarks ( e.g. ,  Set5 ,  Set14 , and  BSD200  ), our extensive evaluations demonstrate the effectiveness of our SR method on achieving both higher restoration quality and computational efficiency compared with several state-of-the-art SR approaches.

Connected through hyperedge HE-1 (induced by source paper "ddet dual path dynamic enhancement network for real world image super resolution"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "lightweight feature fusion network for single image super resolution"
    - "super resolution via image adapted denoising cnns incorporating external and internal learning"
    - "pirm challenge on perceptual image enhancement on smartphones report"
    - "attention aware face hallucination via deep reinforcement learning"
    - "enhanced deep residual networks for single image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "dynamic filter networks"

Connected through hyperedge HE-2 (induced by source paper "drfn deep recurrent fusion network for single image super resolution with large factors"):
  with members:
    - "deep edge guided recurrent residual learning for image super resolution"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "memnet a persistent memory network for image restoration"
    - "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"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"

Connected through hyperedge HE-3 (induced by source paper "difficulty aware image super resolution via deep adaptive dual network"):
  with members:
    - "cross modal attentional context learning for rgb d object detection"
    - "pirm challenge on perceptual image enhancement on smartphones report"
    - "fast and accurate single image super resolution via information distillation network"
    - "image super resolution using very deep residual channel attention networks"
    - "multi level wavelet cnn for image restoration"
    - "attention aware face hallucination via deep reinforcement learning"
    - "enhanced deep residual networks for single image super resolution"
    - "memnet a persistent memory network for image restoration"

Connected through hyperedge HE-4 (induced by source paper "multi level wavelet cnn for image restoration"):
  with members:
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "understanding convolution for semantic segmentation"
    - "memnet a persistent memory network for image restoration"
    - "learning convolutional networks for content weighted image compression"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "learning a single convolutional super resolution network for multiple degradations"):
  with members:
    - "residual dense network for image super resolution"
    - "deep edge guided recurrent residual learning for image super resolution"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "enhanced deep residual networks for single image super resolution"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "memnet a persistent memory network for image restoration"
    - "perceptual losses for real time style transfer and 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)