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: "are generative classifiers more robust to adversarial attacks"
  Abstract: There is a rising interest in studying the robustness of deep neural network classifiers against adversaries, with both advanced attack and defence techniques being actively developed. However, most recent work focuses on discriminative classifiers which only models the conditional distribution of the labels given the inputs. In this abstract we propose deep Bayes classifier that improves the classical naive Bayes with conditional deep generative models, and verifies its robustness against a number of existing attacks. We further developed a detection method for adversarial examples based on conditional deep generative models. Our initial results on MNIST suggest that deep Bayes classifiers might be more robust when compared with deep discriminative classifiers, and the proposed detection method achieves high detection rates against two commonly used attacks.

Connected through hyperedge HE-1 (induced by source paper "anomalous instance detection in deep learning a survey"):
  with members:
    - "unmask adversarial detection and defense through robust feature alignment"
    - "a research agenda dynamic models to defend against correlated attacks"
    - "analysis of confident classifiers for out of distribution detection"
    - "adversarial attacks on time series"
    - "adversarial attacks on deep neural networks for time series classification"
    - "mimicgan robust projection onto image manifolds with corruption mimicking"
    - "analyzing adversarial attacks against deep learning for intrusion detection in iot networks"
    - "deep learning for anomaly detection a survey"

Connected through hyperedge HE-2 (induced by source paper "understanding the limitations of conditional generative models"):
  with members:
    - "hybrid models with deep and invertible features"
    - "semi supervised learning with normalizing flows"
    - "towards the first adversarially robust neural network model on mnist"
    - "darccc detecting adversaries by reconstruction from class conditional capsules"
    - "excessive invariance causes adversarial vulnerability"
    - "i revnet deep invertible networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "adversarial examples are not easily detected bypassing ten detection methods"

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"
    - "conditional generative models are not robust"
    - "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"
    - "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 "on the effectiveness of low frequency perturbations"):
  with members:
    - "adversarial attacks and defences competition"
    - "feature denoising for improving adversarial robustness"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "discrete attacks and submodular optimization with applications to text classification"
    - "towards the first adversarially robust neural network model on mnist"
    - "generating natural language adversarial examples"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "genattack practical black box attacks with gradient free optimization"

Connected through hyperedge HE-5 (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"
    - "conditional generative models are not robust"
    - "generative modeling by estimating gradients of the data distribution"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "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-6 (induced by source paper "conditional generative models are not robust"):
  with members:
    - "towards the first adversarially robust neural network model on mnist"
    - "darccc detecting adversaries by reconstruction from class conditional capsules"
    - "invertible residual networks"
    - "resisting adversarial attacks using gaussian mixture variational autoencoders"
    - "excessive invariance causes adversarial vulnerability"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-7 (induced by source paper "bidirectional learning for robust neural networks"):
  with members:
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "generative adversarial trainer defense to adversarial perturbations with gan"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "adversarial examples attacks and defenses for deep learning"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "towards evaluating the robustness of neural networks"
    - "defensive distillation is not robust to adversarial examples"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

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)