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: "the unreasonable effectiveness of texture transfer for single image super resolution"
  Abstract: While implicit generative models such as GANs have shown impressive results in high quality image reconstruction and manipulation using a combination of various losses, we consider a simpler approach leading to surprisingly strong results. We show that texture loss alone allows the generation of perceptually high quality images. We provide a better understanding of texture constraining mechanism and develop a novel semantically guided texture constraining method for further improvement. Using a recently developed perceptual metric employing "deep features" and termed LPIPS, the method obtains state-of-the-art results. Moreover, we show that a texture representation of those deep features better capture the perceptual quality of an image than the original deep features. Using texture information, off-the-shelf deep classification networks (without training) perform as well as the best performing (tuned and calibrated) LPIPS metrics. The code is publicly available.

Connected through hyperedge HE-1 (induced by source paper "srobb targeted perceptual loss for single image super resolution"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-2 (induced by source paper "photo realistic image super resolution with fast and lightweight cascading residual network"):
  with members:
    - "the relativistic discriminator a key element missing from standard gan"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "nima neural image assessment"
    - "a fully progressive approach to single image super resolution"
    - "residual dense network for image super resolution"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "sketch based manga retrieval using manga109 dataset"

Connected through hyperedge HE-3 (induced by source paper "report on ug 2 challenge track 1 assessing algorithms to improve video object detection and classification from unconstrained mobility platforms"):
  with members:
    - "bridging the gap between computational photography and visual recognition"
    - "vision meets drones a challenge"
    - "yolov3 an incremental improvement"
    - "task driven super resolution object detection in low resolution images"
    - "learning to see in the dark"
    - "enhancing the performance of convolutional neural networks on quality degraded datasets"
    - "learning transferable architectures for scalable image recognition"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (induced by source paper "automatic temporally coherent video colorization"):
  with members:
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "user guided deep anime line art colorization with conditional adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "image colorization using generative adversarial networks"
    - "everybody dance now"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "the 2018 pirm challenge on perceptual image super resolution"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "the relativistic discriminator a key element missing from standard gan"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "generative adversarial network based image super resolution using perceptual content losses"
    - "the perception distortion tradeoff"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-6 (induced by source paper "perceptual deep depth super resolution"):
  with members:
    - "deeply supervised depth map super resolution as novel view synthesis"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "image super resolution using very deep residual channel attention networks"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "generative adversarial network based image super resolution using perceptual content losses"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "joint convolutional neural pyramid for depth map super resolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-7 (induced by source paper "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "assessing shape bias property of convolutional neural networks"
    - "improving shape deformation in unsupervised image to image translation"
    - "one shot texture segmentation"
    - "generalisation in humans and deep neural networks"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"

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)