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 contextual loss for image transformation with non aligned data"
  Abstract: Feed-forward CNNs trained for image transformation problems rely on loss functions that measure the similarity between the generated image and a target image. Most of the common loss functions assume that these images are spatially aligned and compare pixels at corresponding locations. However, for many tasks, aligned training pairs of images will not be available. We present an alternative loss function that does not require alignment, thus providing an effective and simple solution for a new space of problems. Our loss is based on both context and semantics -- it compares regions with similar semantic meaning, while considering the context of the entire image. Hence, for example, when transferring the style of one face to another, it will translate eyes-to-eyes and mouth-to-mouth.

Connected through hyperedge HE-1 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "transgaga geometry aware unsupervised image to image translation"
    - "emergence of object segmentation in perturbed generative models"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "learning conditional deformable templates with convolutional networks"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "voxelmorph a learning framework for deformable medical image registration"

Connected through hyperedge HE-2 (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-3 (induced by source paper "vcnet a robust approach to blind image inpainting"):
  with members:
    - "image inpainting with learnable bidirectional attention maps"
    - "foreground aware image inpainting"
    - "pluralistic image completion"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "a style based generator architecture for generative adversarial networks"
    - "image inpainting via generative multi column convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "neural style difference transfer and its application to font generation"):
  with members:
    - "automatic generation of typographic font from small font subset"
    - "a closed form solution to photorealistic image stylization"
    - "pyramid embedded generative adversarial network for automated font generation"
    - "contained neural style transfer for decorated logo generation"
    - "bringing impressionism to life with neural style transfer in come swim"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "deep photo style transfer"
    - "auto encoder guided gan for chinese calligraphy synthesis"

Connected through hyperedge HE-5 (induced by source paper "deep learning for single image super resolution a brief review"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "fast and accurate single image super resolution via information distillation network"
    - "super resolution based on image adapted cnn denoisers incorporating generalization of training data and internal learning in test time"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "the perception distortion tradeoff"
    - "super resolution via conditional implicit maximum likelihood estimation"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-6 (induced by source paper "singan learning a generative model from a single natural image"):
  with members:
    - "surprising effectiveness of few image unsupervised feature learning"
    - "non stationary texture synthesis by adversarial expansion"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "ingan capturing and remapping the dna of a natural image"
    - "the perception distortion tradeoff"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "attribute driven spontaneous motion in unpaired image translation"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "soft gated warping gan for pose guided person image synthesis"
    - "multimodal unsupervised image to image translation"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "improving shape deformation in unsupervised image to image translation"
    - "instance level facial attributes transfer with geometry aware flow"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "semi parametric image synthesis"

Connected through hyperedge HE-8 (induced by source paper "rethinking lossy compression the rate distortion perception tradeoff"):
  with members:
    - "rate distortion perception tradeoff of variable length source coding for general information sources"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "generative adversarial networks for extreme learned image compression"
    - "maintaining natural image statistics with the contextual loss"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "adversarial feedback loop"
    - "the perception distortion tradeoff"

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)