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: "generative adversarial networks and perceptual losses for video super resolution"
  Abstract: Video super-resolution (VSR) has become one of the most critical problems in video processing. In the deep learning literature, recent works have shown the benefits of using adversarial-based and perceptual losses to improve the performance on various image restoration tasks; however, these have yet to be applied for video super-resolution. In this paper, we propose a generative adversarial network (GAN)-based formulation for VSR. We introduce a new generator network optimized for the VSR problem, named VSRResNet, along with new discriminator architecture to properly guide VSRResNet during the GAN training. We further enhance our VSR GAN formulation with two regularizers, a distance loss in feature-space and pixel-space, to obtain our final VSRResFeatGAN model. We show that pre-training our generator with the mean-squared-error loss only quantitatively surpasses the current state-of-the-art VSR models. Finally, we employ the PercepDist metric to compare the state-of-the-art VSR models. We show that this metric more accurately evaluates the perceptual quality of SR solutions obtained from neural networks, compared with the commonly used PSNR/SSIM metrics. Finally, we show that our proposed model, the VSRResFeatGAN model, outperforms the current state-of-the-art SR models, both quantitatively and qualitatively.

Connected through hyperedge HE-1 (induced by source paper "deep blind video super resolution"):
  with members:
    - "blind super resolution with iterative kernel correction"
    - "blind super resolution kernel estimation using an internal gan"
    - "recurrent back projection network for video super resolution"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "video enhancement with task oriented flow"
    - "neural blind deconvolution using deep priors"
    - "frame recurrent video super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"

Connected through hyperedge HE-2 (induced by source paper "cdgan cyclic discriminative generative adversarial networks for image to image transformation"):
  with members:
    - "csgan cyclic synthesized generative adversarial networks for image to image transformation"
    - "recsys dan discriminative adversarial networks for cross domain recommender systems"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "perceptual adversarial networks for image to image transformation"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "learning representations for automatic colorization"

Connected through hyperedge HE-3 (induced by source paper "a single video super resolution gan for multiple downsampling operators based on pseudo inverse image formation models"):
  with members:
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "learning a single convolutional super resolution network for multiple degradations"
    - "learning deep cnn denoiser prior for image restoration"
    - "detail revealing deep video super resolution"
    - "end to end learning of video super resolution with motion compensation"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-4 (induced by source paper "grim repr prioritising generating important features for pseudo rehearsal"):
  with members:
    - "closed loop memory gan for continual learning"
    - "continual state representation learning for reinforcement learning using generative replay"
    - "pseudo rehearsal achieving deep reinforcement learning without catastrophic forgetting"
    - "deep generative dual memory network for continual learning"
    - "pseudo recursal solving the catastrophic forgetting problem in deep neural networks"
    - "continual reinforcement learning with complex synapses"
    - "progressive neural networks"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-5 (induced by source paper "adversarial defense via local flatness regularization"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "theoretically principled trade off between robustness and accuracy"
    - "interpreting and evaluating neural network robustness"
    - "detection based defense against adversarial examples from the steganalysis point of view"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "densepose dense human pose estimation in the wild"
    - "relation networks for object detection"
    - "you only look once unified real time object detection"

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)