human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "when not to classify anomaly detection of attacks ada on dnn classifiers at test time"):
  Abstract: A significant threat to the recent, wide deployment of machine learning-based systems, including deep neural networks (DNNs), for a host of application domains is adversarial learning (Adv-L) attacks. The main focus here is on exploits applied against (DNN-based) classifiers at test time. While much work has focused on devising attacks that make perturbations to a test pattern (e.g., an image) which are human-imperceptible and yet still induce a change in the classifier's decision, there is relative paucity of work in defending against such attacks. Moreover, our thesis is that most existing defense approaches "miss the mark", seeking to robustify the classifier to make "correct" decisions on perturbed patterns. While, unlike some prior works, we make explicit the motivation of such approaches, we argue that it is generally much more actionable to detect the attack, rather than to "correctly classify" in the face of it. We hypothesize that, even if human-imperceptible, adversarial perturbations are machine-detectable. We propose a purely unsupervised anomaly detector (AD), based on suitable (null hypothesis) density models for the different DNN layers and a novel Kullback-Leibler "distance" AD test statistic. Tested on MNIST and CIFAR10 image databases under the prominent attack strategy proposed by Goodfellow et al. [5], our approach achieves compelling ROC AUCs for attack detection of 0.992 on MNIST, 0.957 on noisy MNIST images, and 0.924 on CIFAR10. We also show that a simple detector that counts the number of white regions in the image achieves 0.97 AUC in detecting the attack on MNIST proposed by Papernot et al. [12].

Includes members:
  Member "wild patterns ten years after the rise of adversarial machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "adversarial examples are not easily detected bypassing ten detection methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "yes machine learning can be more secure a case study on android malware detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards query efficient black box adversary with zeroth order natural gradient descent")
  Member "magnet a two pronged defense against adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial ranking attack and defense")
  Member "safetynet detecting and rejecting adversarial examples robustly":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial ranking attack and defense")
  Member "efficient defenses against adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks")
  Member "adversarial learning a critical review and active learning study":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial learning in statistical classification a comprehensive review of defenses against attacks")
  Member "is deep learning safe for robot vision adversarial examples against the icub humanoid":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "testing and verification of neural network based safety critical control software a systematic literature review")

Question: Please tell me which class the source paper 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)