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: "esrgan enhanced super resolution generative adversarial networks"
  Abstract: The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts. To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block (RRDB) without batch normalization as the basic network building unit. Moreover, we borrow the idea from relativistic GAN to let the discriminator predict relative realness instead of the absolute value. Finally, we improve the perceptual loss by using the features before activation, which could provide stronger supervision for brightness consistency and texture recovery. Benefiting from these improvements, the proposed ESRGAN achieves consistently better visual quality with more realistic and natural textures than SRGAN and won the first place in the PIRM2018-SR Challenge. The code is available at this https URL .

Connected through hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy"):
  with members:
    - "a layer based sequential framework for scene generation with gans"
    - "how generative adversarial networks and their variants work an overview"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "quick and easy time series generation with established image based gans"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "the gan landscape losses architectures regularization and normalization"
    - "deep semantic hashing with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "kindling the darkness a practical low light image enhancer"
    - "residual non local attention networks for image restoration"
    - "making convolutional networks shift invariant again"
    - "a deep journey into super resolution a survey"
    - "selective kernel networks"
    - "deep learning for image super resolution a survey"
    - "real image denoising with feature attention"

Connected through hyperedge HE-3 (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"
    - "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"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "fast accurate and lightweight super resolution with cascading residual network"

Connected through hyperedge HE-4 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions"):
  with members:
    - "spatial attentive single image deraining with a high quality real rain dataset"
    - "progressive image deraining networks a better and simpler baseline"
    - "i can see clearly now image restoration via de raining"
    - "maskgan towards diverse and interactive facial image manipulation"
    - "robust semantic segmentation in adverse weather conditions by means of sensor data fusion"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "single image reflection removal exploiting misaligned training data and network enhancements"
    - "bisenet bilateral segmentation network for real time semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "eventsr from asynchronous events to image reconstruction restoration and super resolution via end to end adversarial learning"):
  with members:
    - "deceiving image to image translation networks for autonomous driving with adversarial perturbations"
    - "events to video bringing modern computer vision to event cameras"
    - "end to end learning of representations for asynchronous event based data"
    - "a deep journey into super resolution a survey"
    - "focus is all you need loss functions for event based vision"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "event based moving object detection and tracking"
    - "the relativistic discriminator a key element missing from standard gan"

Connected through hyperedge HE-6 (induced by source paper "replacing mobile camera isp with a single deep learning model"):
  with members:
    - "ai benchmark all about deep learning on smartphones in 2019"
    - "aim 2019 challenge on image demoireing dataset and study"
    - "semantic image synthesis with spatially adaptive normalization"
    - "image super resolution using very deep residual channel attention networks"
    - "nice noise injection and clamping estimation for neural network quantization"
    - "ai benchmark running deep neural networks on android smartphones"
    - "fast perceptual image enhancement"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-7 (induced by source paper "regularized adaptation for stable and efficient continuous level learning on image processing networks"):
  with members:
    - "cfsnet toward a controllable feature space for image restoration"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "toward convolutional blind denoising of real photographs"
    - "the perception distortion tradeoff"
    - "non local recurrent network for image restoration"

Connected through hyperedge HE-8 (induced by source paper "simultaneous enhancement and super resolution of underwater imagery for improved visual perception"):
  with members:
    - "real time model based image color correction for underwater robots"
    - "deep learning for single image super resolution a brief review"
    - "feedback network for image super resolution"
    - "fast underwater image enhancement for improved visual perception"
    - "an underwater image enhancement benchmark dataset and beyond"
    - "deep visual attention prediction"
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "fast and accurate single image super resolution via information distillation 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)