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: "conditional generative models are not robust"
  Abstract: Class-conditional generative models are an increasingly popular approach to achieve robust classification. They are a natural choice to solve discriminative tasks in a robust manner as they jointly optimize for predictive performance and accurate modeling of the input distribution. In this work, we investigate robust classification with likelihood-based conditional generative models from a theoretical and practical perspective. Our theoretical result reveals that it is impossible to guarantee detectability of adversarial examples even for near-optimal generative classifiers. Experimentally, we show that naively trained conditional generative models have poor discriminative performance, making them unsuitable for classification. This is related to overlooked issues with training conditional generative models and we show methods to improve performance. Finally, we analyze the robustness of our proposed conditional generative models on MNIST and CIFAR10. While we are able to train robust models for MNIST, robustness completely breaks down on CIFAR10. This lack of robustness is related to various undesirable model properties maximum likelihood fails to penalize. Our results indicate that likelihood may fundamentally be at odds with robust classification on challenging problems.

Connected through hyperedge HE-1 (induced by source paper "exact information bottleneck with invertible neural networks getting the best of discriminative and generative modeling"):
  with members:
    - "hexagan generative adversarial nets for real world classification"
    - "sum of squares polynomial flow"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "hybrid models with deep and invertible features"
    - "guided image generation with conditional invertible neural networks"
    - "invertible residual networks"
    - "excessive invariance causes adversarial vulnerability"
    - "mine mutual information neural estimation"

Connected through hyperedge HE-2 (induced by source paper "out of distribution detection with distance guarantee in deep generative models"):
  with members:
    - "are generative deep models for novelty detection truly better"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "group anomaly detection using deep generative models"
    - "deep anomaly detection with outlier exposure"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors"
    - "variational inference of disentangled latent concepts from unlabeled observations"

Connected through hyperedge HE-3 (induced by source paper "reject illegal inputs with generative classifier derived from any discriminative classifier"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial examples are a natural consequence of test error in noise"
    - "adversarial examples improve image recognition"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "are generative classifiers more robust to adversarial attacks"
    - "towards the first adversarially robust neural network model on mnist"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "adversarial example detection and classification with asymmetrical adversarial training"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "natural and adversarial error detection using invariance to image transformations"
    - "consistency regularization for generative adversarial networks"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "towards the first adversarially robust neural network model on mnist"
    - "large scale gan training for high fidelity natural image synthesis"
    - "poison frogs targeted clean label poisoning attacks on neural networks"

Connected through hyperedge HE-5 (induced by source paper "controversial stimuli pitting neural networks against each other as models of human recognition"):
  with members:
    - "mnist c a robustness benchmark for computer vision"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "detecting and diagnosing adversarial images with class conditional capsule reconstructions"
    - "humans can decipher adversarial images"
    - "deep predictive coding network for object recognition"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "towards the first adversarially robust neural network model on mnist"
    - "darccc detecting adversaries by reconstruction from class conditional capsules"

Connected through hyperedge HE-6 (induced by source paper "your classifier is secretly an energy based model and you should treat it like one"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "generative modeling by estimating gradients of the data distribution"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "are generative classifiers more robust to adversarial attacks"
    - "towards the first adversarially robust neural network model on mnist"
    - "spectral normalization for generative adversarial networks"
    - "invertible residual networks"
    - "adversarial distillation of bayesian neural network posteriors"

Connected through hyperedge HE-7 (induced by source paper "adversarial robustness of flow based generative models"):
  with members:
    - "an introduction to variational autoencoders"
    - "feature denoising for improving adversarial robustness"
    - "entropic gans meet vaes a statistical approach to compute sample likelihoods in gans"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "progressive growing of gans for improved quality stability and variation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "density estimation using real nvp"
    - "conditional image generation with pixelcnn decoders"

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)