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: "revealing perceptible backdoors without the training set via the maximum achievable misclassification fraction statistic"
  Abstract: Recently, a special type of data poisoning (DP) attack, known as a backdoor, was proposed. These attacks aimto have a classifier learn to classify to a target class whenever the backdoor pattern is present in a test sample. In thispaper, we address post-training detection of perceptible backdoor patterns in DNN image classifiers, wherein thedefender does not have access to the poisoned training set, but only to the trained classifier itself, as well as to clean(unpoisoned) examples from the classification domain. This problem is challenging since a perceptible backdoorpattern could be any seemingly innocuous object in a scene, and, without the poisoned training set, we have nohint about the actual backdoor pattern used during training. We identify two important properties of perceptiblebackdoor patterns, based upon which we propose a novel detector using the maximum achievable misclassificationfraction (MAMF) statistic. We detect whether the trained DNN has been backdoor-attacked and infer the sourceand target classes used for devising the attack. Our detector, with an easily chosen threshold, is evaluated on fivedatasets, five DNN structures and nine backdoor patterns, and shows strong detection capability. Coupled with animperceptible backdoor detector, our approach helps achieve detection for all evasive backdoors of interest.

Connected through hyperedge HE-1 (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"
    - "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"
    - "backdoor embedding in convolutional neural network models via invisible perturbation"

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)