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: "seeing what a gan cannot generate"
  Abstract: Despite the success of Generative Adversarial Networks (GANs), mode collapse remains a serious issue during GAN training. To date, little work has focused on understanding and quantifying which modes have been dropped by a model. In this work, we visualize mode collapse at both the distribution level and the instance level. First, we deploy a semantic segmentation network to compare the distribution of segmented objects in the generated images with the target distribution in the training set. Differences in statistics reveal object classes that are omitted by a GAN. Second, given the identified omitted object classes, we visualize the GAN's omissions directly. In particular, we compare specific differences between individual photos and their approximate inversions by a GAN. To this end, we relax the problem of inversion and solve the tractable problem of inverting a GAN layer instead of the entire generator. Finally, we use this framework to analyze several recent GANs trained on multiple datasets and identify their typical failure cases.

Connected through hyperedge HE-1 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models"):
  with members:
    - "using videos to evaluate image model robustness"
    - "adversarial examples are a natural consequence of test error in noise"
    - "natural adversarial examples"
    - "image synthesis with a single robust classifier"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "towards photographic image manipulation with balanced growing of generative autoencoders"
    - "optimization and abstraction a synergistic approach for analyzing neural network robustness"
    - "interpreting and evaluating neural network robustness"

Connected through hyperedge HE-2 (induced by source paper "semantic pyramid for image generation"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "on the steerability of generative adversarial networks"
    - "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 "local class specific and global image level generative adversarial networks for semantic guided scene generation"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "a layer based sequential framework for scene generation with gans"
    - "bridging the domain gap for ground to aerial image matching"
    - "singan learning a generative model from a single natural image"
    - "autogan neural architecture search for generative adversarial networks"
    - "coco gan generation by parts via conditional coordinating"
    - "few shot unsupervised image to image translation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"

Connected through hyperedge HE-4 (induced by source paper "learning to have an ear for face super resolution"):
  with members:
    - "what makes training multi modal networks hard"
    - "toward real world single image super resolution a new benchmark and a new model"
    - "two stream action recognition oriented video super resolution"
    - "image super resolution by neural texture transfer"
    - "ranksrgan generative adversarial networks with ranker for image super resolution"
    - "evaluating robustness of deep image super resolution against adversarial attacks"
    - "zoom to learn learn to zoom"
    - "speech2face learning the face behind a voice"

Connected through hyperedge HE-5 (induced by source paper "a plug in method for representation factorization"):
  with members:
    - "wasserstein dependency measure for representation learning"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "generative adversarial networks gans what it can generate and what it cannot"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "neural voice cloning with a few samples"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "pioneer networks progressively growing generative autoencoder"

Connected through hyperedge HE-6 (induced by source paper "image processing using multi code gan prior"):
  with members:
    - "semantic hierarchy emerges in the deep generative representations for scene synthesis"
    - "few shot unsupervised image to image translation"
    - "on the steerability of generative adversarial networks"
    - "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-7 (induced by source paper "cnn generated images are surprisingly easy to spot for now"):
  with members:
    - "detecting gan generated fake images using co occurrence matrices"
    - "faceforensics learning to detect manipulated facial images"
    - "analyzing and improving the image quality of stylegan"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "detecting and simulating artifacts in gan fake images"
    - "fakespotter a simple yet robust baseline for spotting ai synthesized fake faces"
    - "semantic image synthesis with spatially adaptive normalization"
    - "learning rich features for image manipulation detection"

Connected through hyperedge HE-8 (induced by source paper "image2stylegan how to edit the embedded images"):
  with members:
    - "detecting overfitting of deep generative networks via latent recovery"
    - "image2stylegan how to embed images into the stylegan latent space"
    - "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"

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)