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: "defend deep neural networks against adversarial examples via fixed anddynamic quantized activation functions"
  Abstract: Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks. To this end, many defense approaches that attempt to improve the robustness of DNNs have been proposed. In a separate and yet related area, recent works have explored to quantize neural network weights and activation functions into low bit-width to compress model size and reduce computational complexity. In this work,we find that these two different tracks, namely the pursuit of network compactness and robustness, can bemerged into one and give rise to networks of both advantages. To the best of our knowledge, this is the first work that uses quantization of activation functions to defend against adversarial examples. We also propose to train robust neural networks by using adaptive quantization techniques for the activation functions. Our proposed Dynamic Quantized Activation (DQA) is verified through a wide range of experiments with the MNIST and CIFAR-10 datasets under different white-box attack methods, including FGSM, PGD, andC&W attacks. Furthermore, Zeroth Order Optimization and substitute model based black-box attacks are also considered in this work. The experimental results clearly show that the robustness of DNNs could be greatly improved using the proposed DQA.

Connected through hyperedge HE-1 (induced by source paper "impact of low bitwidth quantization on the adversarial robustness for embedded neural networks"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "defensive quantization when efficiency meets robustness"
    - "on evaluating adversarial robustness"
    - "defending against whitebox adversarial attacks via randomized discretization"
    - "adversarial examples a complete characterisation of the phenomenon"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "stochastic activation pruning for robust adversarial defense"
    - "efficient two step adversarial defense for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "metric learning for adversarial robustness"):
  with members:
    - "hardness aware deep metric learning"
    - "are labels required for improving adversarial robustness"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "an analysis of the t sne algorithm for data visualization"
    - "formal security analysis of neural networks using symbolic intervals"
    - "logit pairing methods can fool gradient based attacks"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-3 (induced by source paper "defensive quantization when efficiency meets robustness"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "l2 nonexpansive neural networks"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "robust sparse regularization simultaneously optimizing neural network robustness and compactness"):
  with members:
    - "adversarial robustness vs model compression or both"
    - "defensive quantization when efficiency meets robustness"
    - "sparse dnns with improved adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "structured pruning for efficient convnets via incremental regularization"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "a roadmap toward the resilient internet of things for cyber physical systems"):
  with members:
    - "model based verification and validation of an autonomous vehicle system"
    - "deepmarks a digital fingerprinting framework for deep neural networks"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "reachability analysis and safety verification for neural network control systems"
    - "introduction of a hybrid monitor to cyber physical systems"
    - "verification for machine learning autonomy and neural networks survey"
    - "re purposing compact neuronal circuit policies to govern reinforcement learning tasks"
    - "efficient deep learning on multi source private data"

Connected through hyperedge HE-6 (induced by source paper "qusecnets quantization based defense mechanism for securing deep neural network against adversarial attacks"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "ape gan adversarial perturbation elimination with gan"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"
    - "simple black box adversarial perturbations for deep networks"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "towards evaluating the robustness of neural 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.LG(Machine Learning)