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: "classification accuracy score for conditional generative models"
  Abstract: Deep generative models (DGMs) of images are now sufficiently mature that they produce nearly photorealistic samples and obtain scores similar to the data distribution on heuristics such as Frechet Inception Distance (FID). These results, especially on large-scale datasets such as ImageNet, suggest that DGMs are learning the data distribution in a perceptually meaningful space and can be used in downstream tasks. To test this latter hypothesis, we use class-conditional generative models from a number of model classes---variational autoencoders, autoregressive models, and generative adversarial networks (GANs)---to infer the class labels of real data. We perform this inference by training an image classifier using only synthetic data and using the classifier to predict labels on real data. The performance on this task, which we call Classification Accuracy Score (CAS), reveals some surprising results not identified by traditional metrics and constitute our contributions. First, when using a state-of-the-art GAN (BigGAN-deep), Top-1 and Top-5 accuracy decrease by 27.9\% and 41.6\%, respectively, compared to the original data; and conditional generative models from other model classes, such as Vector-Quantized Variational Autoencoder-2 (VQ-VAE-2) and Hierarchical Autoregressive Models (HAMs), substantially outperform GANs on this benchmark. Second, CAS automatically surfaces particular classes for which generative models failed to capture the data distribution, and were previously unknown in the literature. Third, we find traditional GAN metrics such as Inception Score (IS) and FID neither predictive of CAS nor useful when evaluating non-GAN models. Furthermore, in order to facilitate better diagnoses of generative models, we open-source the proposed metric.

Connected through hyperedge HE-1 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "on mutual information maximization for representation learning"
    - "large scale adversarial representation learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "do imagenet classifiers generalize to imagenet"
    - "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-2 (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"
    - "few shot unsupervised image to image translation"

Connected through hyperedge HE-3 (induced by source paper "iterative energy based projection on a normal data manifold for anomaly localization"):
  with members:
    - "unsupervised anomaly localization using variational auto encoders"
    - "improving unsupervised defect segmentation by applying structural similarity to autoencoders"
    - "diagnosing and enhancing vae models"
    - "improving generalization and stability of generative adversarial networks"
    - "deep structured energy based image inpainting"
    - "anomaly machine component detection by deep generative model with unregularized score"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "on the evaluation of conditional gans"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "diversity sensitive conditional generative adversarial networks"
    - "from variational to deterministic autoencoders"
    - "hype a benchmark for human eye perceptual evaluation of generative models"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"

Connected through hyperedge HE-5 (induced by source paper "semantic object accuracy for generative text to image synthesis"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "interactive image generation using scene graphs"
    - "realistic image generation using region phrase attention"
    - "object driven text to image synthesis via adversarial training"
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "image synthesis from reconfigurable layout and style"
    - "visual relation conscious image generation from structured text"
    - "layoutvae stochastic scene layout generation from a label set"

Connected through hyperedge HE-6 (induced by source paper "randaugment practical automated data augmentation with a reduced search space"):
  with members:
    - "starnet targeted computation for object detection in point clouds"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "instaboost boosting instance segmentation via probability map guided copy pasting"
    - "fast autoaugment"
    - "do imagenet classifiers generalize to imagenet"
    - "unsupervised data augmentation for consistency training"

Connected through hyperedge HE-7 (induced by source paper "randaugment practical data augmentation with no separate search"):
  with members:
    - "starnet targeted computation for object detection in point clouds"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "do imagenet classifiers generalize to imagenet"
    - "a fourier perspective on model robustness in computer vision"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "learning data augmentation strategies for object detection"

Connected through hyperedge HE-8 (induced by source paper "an empirical study of batch normalization and group normalization in conditional computation"):
  with members:
    - "batch normalization is a cause of adversarial vulnerability"
    - "fixup initialization residual learning without normalization"
    - "group normalization"
    - "systematic generalization what is required and can it be learned"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "modulating early visual processing by language"

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.LG(Machine Learning)