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 "exploring the back alleys analysing the robustness of alternative neural network architectures against adversarial attacks"):
  Abstract: Recent discoveries in the field of adversarial machine learning have shown that Artificial Neural Networks (ANNs) are susceptible to adversarial attacks. These attacks cause misclassification of specially crafted adversarial samples. In light of this phenomenon, it is worth investigating whether other types of neural networks are less susceptible to adversarial attacks. In this work, we applied standard attack methods originally aimed at conventional ANNs, towards stochastic ANNs and also towards Spiking Neural Networks (SNNs), across three different datasets namely MNIST, CIFAR-10 and Patch Camelyon. We analysed their adversarial robustness against attacks performed in the raw image space of the different model variants. We employ a variety of attacks namely Basic Iterative Method (BIM), Carlini & Wagner L2 attack (CWL2) and Boundary attack. Our results suggests that SNNs and stochastic ANNs exhibit some degree of adversarial robustness as compared to their ANN counterparts under certain attack methods. Namely, we found that the Boundary and the state-of-the-art CWL2 attacks are largely ineffective against stochastic ANNs. Following this observation, we proposed a modified version of the CWL2 attack and analysed the impact of this attack on the models' adversarial robustness. Our results suggest that with this modified CWL2 attack, many models are more easily fooled as compared to the vanilla CWL2 attack, albeit observing an increase in L2 norms of adversarial perturbations. Lastly, we also investigate the resilience of alternative neural networks against adversarial samples transferred from ResNet18. We show that the modified CWL2 attack provides an improved cross-architecture transferability compared to other attacks.

Includes members:
  Member "adversarial attacks on remote user authentication using behavioural mouse dynamics":
    also connected through other hyperedges:
      (none)
  Member "a novel method for extracting interpretable knowledge from a spiking neural classifier with time varying synaptic weights":
    also connected through other hyperedges:
      (none)
  Member "a comprehensive analysis on adversarial robustness of spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploring adversarial attack in spiking neural networks with spike compatible gradient")
  Member "improving black box adversarial attacks with a transfer based prior":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "geoda a geometric framework for black box adversarial attacks")
  Member "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "s4nn temporal backpropagation for spiking neural networks with one spike per neuron")
  Member "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification")
  Member "enabling spike based backpropagation in state of the art deep neural network architectures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rmp snns residual membrane potential neuron for enabling deeper high accuracy and low latency spiking neural networks")
  Member "combinatorial attacks on binarized neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")

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)