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: "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].

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 "revealing backdoors post training in dnn classifiers via novel inference on optimized perturbations inducing group misclassification"):
  with members:
    - "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "backdoor embedding in convolutional neural network models via invisible perturbation"
    - "spectral signatures in backdoor attacks"
    - "when not to classify detection of reverse engineering attacks on dnn image classifiers"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-3 (induced by source paper "motivating the rules of the game for adversarial example research"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "machine vs machine minimax optimal defense against adversarial examples"
    - "scaling provable adversarial defenses"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "stochastic activation pruning for robust adversarial defense"
    - "adversarial patch"
    - "bidirectional learning for robust neural networks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"

Connected through hyperedge HE-4 (induced by source paper "when not to classify detection of reverse engineering attacks on dnn image classifiers"):
  with members:
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "adversarial learning a critical review and active learning study"
    - "practical black box attacks against machine learning"
    - "stealing machine learning models via prediction apis"
    - "towards evaluating the robustness of neural networks"
    - "the limitations of deep learning in adversarial settings"

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)