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: "a learning and masking approach to secure learning"
  Abstract: Deep Neural Networks (DNNs) have been shown to be vulnerable against adversarial examples, which are data points cleverly constructed to fool the classifier. Such attacks can be devastating in practice, especially as DNNs are being applied to ever increasing critical tasks like image recognition in autonomous driving. In this paper, we introduce a new perspective on the problem. We do so by first defining robustness of a classifier to adversarial exploitation. Next, we show that the problem of adversarial example generation can be posed as learning problem. We also categorize attacks in literature into high and low perturbation attacks; well-known attacks like fast-gradient sign method (FGSM) and our attack produce higher perturbation adversarial examples while the more potent but computationally inefficient Carlini-Wagner (CW) attack is low perturbation. Next, we show that the dual approach of the attack learning problem can be used as a defensive technique that is effective against high perturbation attacks. Finally, we show that a classifier masking method achieved by adding noise to the a neural network's logit output protects against low distortion attacks such as the CW attack. We also show that both our learning and masking defense can work simultaneously to protect against multiple attacks. We demonstrate the efficacy of our techniques by experimenting with the MNIST and CIFAR-10 datasets.

Connected through hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey"):
  with members:
    - "securing future autonomous connected vehicles challenges posed by adversarial machine learning and the way forward"
    - "reproducibility in machine learning for health"
    - "fairness in algorithmic decision making an excursion through the lens of causality"
    - "caveat emptor the risks of using big data for human development"
    - "motion corrected multishot mri reconstruction using generative networks with sensitivity encoding"
    - "the adversarial machine learning conundrum can the insecurity of ml become the achilles heel of cognitive networks"
    - "self supervised domain adaptation for computer vision tasks"
    - "feature robustness in non stationary health records caveats to deployable model performance in common clinical machine learning tasks"

Connected through hyperedge HE-2 (induced by source paper "defending against adversarial attacks using random forests"):
  with members:
    - "exploring the space of black box attacks on deep neural networks"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "countering adversarial images using input transformations"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-3 (induced by source paper "automated detection system for adversarial examples with high frequency noises sieve"):
  with members:
    - "generating adversarial examples with adversarial networks"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "magnet a two pronged defense against adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-4 (induced by source paper "defense vae a fast and accurate defense against adversarial attacks"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "magnet a two pronged defense against adversarial examples"
    - "deepcloak masking deep neural network models for robustness against adversarial samples"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "countering adversarial images using input transformations"
    - "defense against universal adversarial perturbations"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-5 (induced by source paper "machine vs machine minimax optimal defense against adversarial examples"):
  with members:
    - "magnet a two pronged defense against adversarial examples"
    - "the numerics of gans"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "stabilizing training of generative adversarial networks through regularization"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "analysis of universal adversarial perturbations"
    - "gradient descent gan optimization is locally stable"
    - "minimax filter learning to preserve privacy from inference attacks"

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.CR(Cryptography and Security)