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: "image2stylegan how to embed images into the stylegan latent space"
  Abstract: We propose an efficient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHQ dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of the embedding algorithm provides valuable insights into the structure of the StyleGAN latent space. We propose a set of experiments to test what class of images can be embedded, how they are embedded, what latent space is suitable for embedding, and if the embedding is semantically meaningful.

Connected through hyperedge HE-1 (induced by source paper "stylegan2 distillation for feed forward image manipulation"):
  with members:
    - "style generator inversion for image enhancement and animation"
    - "analyzing and improving the image quality of stylegan"
    - "learning continuous face age progression a pyramid of gans"
    - "face to parameter translation for game character auto creation"
    - "meta sim learning to generate synthetic datasets"
    - "stargan v2 diverse image synthesis for multiple domains"
    - "classification accuracy score for conditional generative models"
    - "few shot unsupervised image to image translation"

Connected through hyperedge HE-2 (induced by source paper "deep automodulators"):
  with members:
    - "implicit autoencoders"
    - "analyzing and improving the image quality of stylegan"
    - "large scale adversarial representation learning"
    - "generating diverse high fidelity images with vq vae 2"
    - "towards photographic image manipulation with balanced growing of generative autoencoders"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "pioneer networks progressively growing generative autoencoder"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "sean image synthesis with semantic region adaptive normalization"):
  with members:
    - "controllable artistic text style transfer via shape matching gan"
    - "maskgan towards diverse and interactive facial image manipulation"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "mask guided portrait editing with conditional gans"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "non stationary texture synthesis by adversarial expansion"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-4 (induced by source paper "mimicgan robust projection onto image manifolds with corruption mimicking"):
  with members:
    - "efficient gan based anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "universal decision based black box perturbations breaking security through obscurity defenses"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "nam non adversarial unsupervised domain mapping"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "why do deep convolutional networks generalize so poorly to small image transformations"

Connected through hyperedge HE-5 (induced by source paper "achieving robustness in the wild via adversarial mixing with disentangled representations"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "natural adversarial examples"
    - "theoretically principled trade off between robustness and accuracy"
    - "do imagenet classifiers generalize to imagenet"
    - "feature denoising for improving adversarial robustness"
    - "robustness via curvature regularization and vice versa"
    - "adversarial logit pairing"

Connected through hyperedge HE-6 (induced by source paper "image2stylegan how to edit the embedded images"):
  with members:
    - "seeing what a gan cannot generate"
    - "detecting overfitting of deep generative networks via latent recovery"
    - "few shot video to video synthesis"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "semantic image synthesis with spatially adaptive normalization"
    - "video to video synthesis"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "analyzing and improving the image quality of stylegan"):
  with members:
    - "style generator inversion for image enhancement and animation"
    - "source generator attribution via inversion"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "msg gan multi scale gradient gan for stable image synthesis"
    - "interpreting the latent space of gans for semantic face editing"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "attributing fake images to gans analyzing fingerprints in generated images"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "transflow learning repurposing flow models without retraining"):
  with members:
    - "few shot unsupervised image to image translation"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "latent constraints learning to generate conditionally from unconditional generative models"
    - "density estimation using real nvp"
    - "conditional image generation with pixelcnn decoders"
    - "adversarial feature learning"
    - "a neural algorithm of artistic style"

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)