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 "snn under attack are spiking deep belief networks vulnerable to adversarial examples"):
  Abstract: Recently, many adversarial examples have emerged for Deep Neural Networks (DNNs) causing misclassifications. However, in-depth work still needs to be performed to demonstrate such attacks and security vulnerabilities for spiking neural networks (SNNs), i.e. the 3rd generation NNs. This paper aims at addressing the fundamental questions:"Are SNNs vulnerable to the adversarial attacks as well?" and "if yes, to what extent?" Using a Spiking Deep Belief Network (SDBN) for the MNIST database classification, we show that the SNN accuracy decreases accordingly to the noise magnitude in data poisoning random attacks applied to the test images. Moreover, SDBNs generalization capabilities increase by applying noise to the training images. We develop a novel black box attack methodology to automatically generate imperceptible and robust adversarial examples through a greedy algorithm, which is first of its kind for SNNs.

Includes members:
  Member "deep learning in spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a supervised learning algorithm for multilayer spiking neural networks based on temporal coding toward energy efficient vlsi processor design")
  Member "adversarial examples opportunities and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ai safety state of the field through quantitative lens")
  Member "towards imperceptible and robust adversarial example attacks against neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations")
  Member "are adversarial examples inevitable":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "fademl understanding the impact of pre processing noise filtering on adversarial machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial security attacks and perturbations on machine learning and deep learning methods")
  Member "weighted contrastive divergence":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "accelerating deep learning with memcomputing")
  Member "enhancing robustness of machine learning systems via data transformations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space")
  Member "evt_mnist a spike based version of traditional mnist":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "time2vec learning a vector representation of time")

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)