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: "on the steerability of generative adversarial networks"
  Abstract: An open secret in contemporary machine learning is that many models work beautifully on standard benchmarks but fail to generalize outside the lab. This has been attributed to biased training data, which provide poor coverage over real world events. Generative models are no exception, but recent advances in generative adversarial networks (GANs) suggest otherwise - these models can now synthesize strikingly realistic and diverse images. Is generative modeling of photos a solved problem? We show that although current GANs can fit standard datasets very well, they still fall short of being comprehensive models of the visual manifold. In particular, we study their ability to fit simple transformations such as camera movements and color changes. We find that the models reflect the biases of the datasets on which they are trained (e.g., centered objects), but that they also exhibit some capacity for generalization: by "steering" in latent space, we can shift the distribution while still creating realistic images. We hypothesize that the degree of distributional shift is related to the breadth of the training data distribution. Thus, we conduct experiments to quantify the limits of GAN transformations and introduce techniques to mitigate the problem. Code is released on our project page: https://ali-design.github.io/gan_steerability/

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 "semantic pyramid for image generation"):
  with members:
    - "seeing what a gan cannot generate"
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "image resizing by reconstruction from deep features"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "controlling generative models with continuous factors of variations"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "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"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-4 (induced by source paper "unsupervised discovery of interpretable directions in the gan latent space"):
  with members:
    - "high fidelity synthesis with disentangled representation"
    - "salient object detection in the deep learning era an in depth survey"
    - "semantic hierarchy emerges in the deep generative representations for scene synthesis"
    - "rpgan gans interpretability via random routing"
    - "interpreting the latent space of gans for semantic face editing"
    - "oogan disentangling gan with one hot sampling and orthogonal regularization"
    - "video to video synthesis"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "image processing using multi code gan prior"):
  with members:
    - "seeing what a gan cannot generate"
    - "semantic hierarchy emerges in the deep generative representations for scene synthesis"
    - "few shot unsupervised image to image translation"
    - "gated gan adversarial gated networks for multi collection style transfer"
    - "interpreting the latent space of gans for semantic face editing"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "latent convolutional models"

Connected through hyperedge HE-6 (induced by source paper "interpreting the latent space of gans for semantic face editing"):
  with members:
    - "lia latently invertible autoencoder with adversarial learning"
    - "ganalyze toward visual definitions of cognitive image properties"
    - "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-7 (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"
    - "ganalyze toward visual definitions of cognitive image properties"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "improving sample diversity of a pre trained class conditional gan by changing its class embeddings"):
  with members:
    - "generative adversarial networks a survey and taxonomy"
    - "diversity sensitive conditional generative adversarial networks"
    - "understanding neural networks via feature visualization a survey"
    - "pros and cons of gan evaluation measures"
    - "transferring gans generating images from limited data"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "progressive growing of gans for improved quality stability and variation"

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)