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: "efficient defenses against adversarial attacks"
  Abstract: Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat. Adversarial samples are crafted with a deliberate intention of undermining a system. In the case of DNNs, the lack of better understanding of their working has prevented the development of efficient defenses. In this paper, we propose a new defense method based on practical observations which is easy to integrate into models and performs better than state-of-the-art defenses. Our proposed solution is meant to reinforce the structure of a DNN, making its prediction more stable and less likely to be fooled by adversarial samples. We conduct an extensive experimental study proving the efficiency of our method against multiple attacks, comparing it to numerous defenses, both in white-box and black-box setups. Additionally, the implementation of our method brings almost no overhead to the training procedure, while maintaining the prediction performance of the original model on clean samples.

Connected through hyperedge HE-1 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks"):
  with members:
    - "fastwordbug a fast method to generate adversarial text against nlp applications"
    - "transferability of adversarial examples to attack cloud based image classifier service"
    - "robust audio adversarial example for a physical attack"
    - "daedalus breaking non maximum suppression in object detection via adversarial examples"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "a tour of convolutional networks guided by linear interpreters"
    - "adversarial examples versus cloud based detectors a black box empirical study"
    - "adversarial t shirt evading person detectors in a physical world"

Connected through hyperedge HE-2 (induced by source paper "visionguard runtime detection of adversarial inputs to perception systems"):
  with members:
    - "detection based defense against adversarial examples from the steganalysis point of view"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "on the limitation of local intrinsic dimensionality for characterizing the subspaces of adversarial examples"
    - "lavan localized and visible adversarial noise"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-3 (induced by source paper "cat customized adversarial training for improved robustness"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "sign opt a query efficient hard label adversarial attack"
    - "label smoothing and logit squeezing a replacement for adversarial training"
    - "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "certified adversarial robustness via randomized smoothing"
    - "convergence of adversarial training in overparametrized neural networks"
    - "theoretically principled trade off between robustness and accuracy"

Connected through hyperedge HE-4 (induced by source paper "analyzing accuracy loss in randomized smoothing defenses"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "certified adversarial robustness with additive noise"
    - "certified defenses against adversarial examples"
    - "towards robust neural networks via random self ensemble"
    - "understanding deep learning requires rethinking generalization"
    - "towards evaluating the robustness of neural networks"
    - "robustness of classifiers from adversarial to random noise"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-5 (induced by source paper "security and privacy approaches in mixed reality a literature survey"):
  with members:
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "magnet a two pronged defense against adversarial examples"
    - "demo recon revealing and controlling pii leaks in mobile network traffic"
    - "cryptoimg privacy preserving processing over encrypted images"
    - "cardea context aware visual privacy protection from pervasive cameras"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "risk perceptions for wearable devices"
    - "rappor randomized aggregatable privacy preserving ordinal response"

Connected through hyperedge HE-6 (induced by source paper "colored noise injection for training adversarially robust neural networks"):
  with members:
    - "training robust deep neural networks via adversarial noise propagation"
    - "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "defending against whitebox adversarial attacks via randomized discretization"
    - "adversarial training with voronoi constraints"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "learning to defense by learning to attack"
    - "delving into transferable adversarial examples and black box attacks"

Connected through hyperedge HE-7 (induced by source paper "understanding artificial intelligence ethics and safety"):
  with members:
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "model cards for model reporting"
    - "a framework for understanding unintended consequences of machine learning"
    - "spectrum data poisoning with adversarial deep learning"
    - "explaining explanations in ai"
    - "problem formulation and fairness"

Connected through hyperedge HE-8 (induced by source paper "adversarial learning in statistical classification a comprehensive review of defenses against attacks"):
  with members:
    - "revealing backdoors post training in dnn classifiers via novel inference on optimized perturbations inducing group misclassification"
    - "strip a defence against trojan attacks on deep neural networks"
    - "defensive quantization when efficiency meets robustness"
    - "revealing perceptible backdoors without the training set via the maximum achievable misclassification fraction statistic"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "a mixture model based defense for data poisoning attacks against naive bayes spam filters"

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)