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: "ganalyze toward visual definitions of cognitive image properties"
  Abstract: We introduce a framework that uses Generative Adversarial Networks (GANs) to study cognitive properties like memorability, aesthetics, and emotional valence. These attributes are of interest because we do not have a concrete visual definition of what they entail. What does it look like for a dog to be more or less memorable? GANs allow us to generate a manifold of natural-looking images with fine-grained differences in their visual attributes. By navigating this manifold in directions that increase memorability, we can visualize what it looks like for a particular generated image to become more or less memorable. The resulting ``visual definitions" surface image properties (like ``object size") that may underlie memorability. Through behavioral experiments, we verify that our method indeed discovers image manipulations that causally affect human memory performance. We further demonstrate that the same framework can be used to analyze image aesthetics and emotional valence. Visit the GANalyze website at this http URL.

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"
    - "image2stylegan how to embed images into the stylegan latent space"
    - "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"

Connected through hyperedge HE-2 (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"
    - "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"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-3 (induced by source paper "semantic hierarchy emerges in the deep generative representations for scene synthesis"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "singan learning a generative model from a single natural image"
    - "edge semantic learning strategy for layout estimation in indoor environment"
    - "semantic image synthesis with spatially adaptive normalization"
    - "interpreting the latent space of gans for semantic face editing"
    - "3d aware scene manipulation via inverse graphics"
    - "unified perceptual parsing for scene understanding"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "interpreting the latent space of gans for semantic face editing"):
  with members:
    - "lia latently invertible autoencoder with adversarial learning"
    - "on the steerability of generative adversarial networks"
    - "facefeat gan a two stage approach for identity preserving face synthesis"
    - "spectral normalization for generative adversarial networks"
    - "towards open set identity preserving face synthesis"
    - "latent space non linear statistics"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-5 (induced by source paper "on the steerability of generative adversarial networks"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "flat metric minimization with applications in generative modeling"
    - "detecting bias with generative counterfactual face attribute augmentation"
    - "interpreting the latent space of gans for semantic face editing"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-6 (induced by source paper "this dataset does not exist training models from generated images"):
  with members:
    - "domain specific batch normalization for unsupervised domain adaptation"
    - "classification accuracy score for conditional generative models"
    - "on the steerability of generative adversarial networks"
    - "interpreting the latent space of gans for semantic face editing"
    - "how good is my gan"
    - "spectral normalization for generative adversarial networks"
    - "bagan data augmentation with balancing gan"
    - "large scale gan training for high fidelity natural image synthesis"

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)