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 visual manipulation on the natural image manifold"
  Abstract: Realistic image manipulation is challenging because it requires modifying the image appearance in a user-controlled way, while preserving the realism of the result. Unless the user has considerable artistic skill, it is easy to "fall off" the manifold of natural images while editing. In this paper, we propose to learn the natural image manifold directly from data using a generative adversarial neural network. We then define a class of image editing operations, and constrain their output to lie on that learned manifold at all times. The model automatically adjusts the output keeping all edits as realistic as possible. All our manipulations are expressed in terms of constrained optimization and are applied in near-real time. We evaluate our algorithm on the task of realistic photo manipulation of shape and color. The presented method can further be used for changing one image to look like the other, as well as generating novel imagery from scratch based on user's scribbles.

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 "adversarial code learning for image generation"):
  with members:
    - "image transformer"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "semi supervised learning with gans revisiting manifold regularization"
    - "large scale gan training for high fidelity natural image synthesis"
    - "dist gan an improved gan using distance constraints"
    - "mcgan mean and covariance feature matching gan"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-3 (induced by source paper "fine grained image to image transformation towards visual recognition"):
  with members:
    - "a two step disentanglement method"
    - "hologan unsupervised learning of 3d representations from natural images"
    - "foreground aware image inpainting"
    - "carigan caricature generation through weakly paired adversarial learning"
    - "visual object networks image generation with disentangled 3d representation"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "cr gan learning complete representations for multi view generation"
    - "multi task convolutional neural network for pose invariant face recognition"

Connected through hyperedge HE-4 (induced by source paper "adacof adaptive collaboration of flows for video frame interpolation"):
  with members:
    - "depth aware video frame interpolation"
    - "phasenet for video frame interpolation"
    - "the perception distortion tradeoff"
    - "memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement"
    - "context aware synthesis for video frame interpolation"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "unsupervised learning of disentangled representations from video"
    - "video frame interpolation via adaptive convolution"

Connected through hyperedge HE-5 (induced by source paper "discriminator feature based inference by recycling the discriminator of gans"):
  with members:
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "pluralistic image completion"
    - "improved training of generative adversarial networks using representative features"
    - "group normalization"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "which training methods for gans do actually converge"

Connected through hyperedge HE-6 (induced by source paper "minimax defense against gradient based adversarial attacks"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial robustness toolbox v1 0 0"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "magnet a two pronged defense against adversarial examples"
    - "ape gan adversarial perturbation elimination with gan"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"

Connected through hyperedge HE-7 (induced by source paper "controlling generative models with continuous factors of variations"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "on the steerability of generative adversarial networks"
    - "ganalyze toward visual definitions of cognitive image properties"
    - "deeply supervised salient object detection with short connections"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "inverting the generator of a generative adversarial network ii"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-8 (induced by source paper "large scale optimal transport via adversarial training with cycle consistency"):
  with members:
    - "on scalable and efficient computation of large scale optimal transport"
    - "adversarial computation of optimal transport maps"
    - "optimal transport mapping via input convex neural networks"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "guiding the one to one mapping in cyclegan via optimal transport"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning to discover cross domain relations with generative adversarial networks"

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)