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: "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.

Connected through hyperedge HE-1 (induced by source paper "exploring adversarial attack in spiking neural networks with spike compatible gradient"):
  with members:
    - "a comprehensive analysis on adversarial robustness of spiking neural networks"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "adversarial patch"
    - "direct training for spiking neural networks faster larger better"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "robust physical world attacks on deep learning models"

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)