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: "srpgan perceptual generative adversarial network for single image super resolution"
  Abstract: Single image super resolution (SISR) is to reconstruct a high resolution image from a single low resolution image. The SISR task has been a very attractive research topic over the last two decades. In recent years, convolutional neural network (CNN) based models have achieved great performance on SISR task. Despite the breakthroughs achieved by using CNN models, there are still some problems remaining unsolved, such as how to recover high frequency details of high resolution images. Previous CNN based models always use a pixel wise loss, such as l2 loss. Although the high resolution images constructed by these models have high peak signal-to-noise ratio (PSNR), they often tend to be blurry and lack high-frequency details, especially at a large scaling factor. In this paper, we build a super resolution perceptual generative adversarial network (SRPGAN) framework for SISR tasks. In the framework, we propose a robust perceptual loss based on the discriminator of the built SRPGAN model. We use the Charbonnier loss function to build the content loss and combine it with the proposed perceptual loss and the adversarial loss. Compared with other state-of-the-art methods, our method has demonstrated great ability to construct images with sharp edges and rich details. We also evaluate our method on different benchmarks and compare it with previous CNN based methods. The results show that our method can achieve much higher structural similarity index (SSIM) scores on most of the benchmarks than the previous state-of-art methods.

Connected through hyperedge HE-1 (induced by source paper "benefiting from multitask learning to improve single image super resolution"):
  with members:
    - "learn to synthesize and synthesize to learn"
    - "using photorealistic face synthesis and domain adaptation to improve facial expression analysis"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "image super resolution using very deep residual channel attention networks"
    - "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"
    - "learning to maintain natural image statistics"
    - "video deblurring via semantic segmentation and pixel wise non linear kernel"
    - "photographic image synthesis with cascaded refinement networks"

Connected through hyperedge HE-2 (induced by source paper "attribute guided unpaired image to image translation with semi supervised learning"):
  with members:
    - "high fidelity image generation with fewer labels"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "smit stochastic multi label image to image translation"
    - "multimodal unsupervised image to image translation"
    - "isolating sources of disentanglement in variational autoencoders"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "ganimation anatomically aware facial animation from a single image"

Connected through hyperedge HE-3 (induced by source paper "generalization in generative adversarial networks a novel perspective from privacy protection"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "a style based generator architecture for generative adversarial networks"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "generalization and equilibrium in generative adversarial nets gans"

Connected through hyperedge HE-4 (induced by source paper "unsupervised attention guided image to image translation"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "bagan data augmentation with balancing gan"
    - "learn to pay attention"
    - "image to image translation for domain adaptation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "lr gan layered recursive generative adversarial networks for image generation"

Connected through hyperedge HE-5 (induced by source paper "multimodal sensor fusion in single thermal image super resolution"):
  with members:
    - "densely connected high order residual network for single frame image super resolution"
    - "face super resolution through wasserstein gans"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "nips 2016 tutorial generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"

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)