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 "capsattacks robust and imperceptible adversarial attacks on capsule networks"):
  Abstract: Capsule Networks preserve the hierarchical spatial relationships between objects, and thereby bears a potential to surpass the performance of traditional Convolutional Neural Networks (CNNs) in performing tasks like image classification. A large body of work has explored adversarial examples for CNNs, but their effectiveness on Capsule Networks has not yet been well studied. In our work, we perform an analysis to study the vulnerabilities in Capsule Networks to adversarial attacks. These perturbations, added to the test inputs, are small and imperceptible to humans, but can fool the network to mispredict. We propose a greedy algorithm to automatically generate targeted imperceptible adversarial examples in a black-box attack scenario. We show that this kind of attacks, when applied to the German Traffic Sign Recognition Benchmark (GTSRB), mislead Capsule Networks. Moreover, we apply the same kind of adversarial attacks to a 5-layer CNN and a 9-layer CNN, and analyze the outcome, compared to the Capsule Networks to study differences in their behavior.

Includes members:
  Member "towards imperceptible and robust adversarial example attacks against neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations")
  Member "capsnet comparative performance evaluation for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "enabling highly efficient capsule networks processing through a pim based architecture design")
  Member "novel deep learning model for traffic sign detection using capsule networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "examining the benefits of capsule neural networks")
  Member "darccc detecting adversaries by reconstruction from class conditional capsules":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding the limitations of conditional generative models")
  Member "are adversarial examples inevitable":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "dolphinattack inaudible voice commands":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ghostimage perception domain attacks against vision based object classification systems")
  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 "dynamic routing between capsules":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lightweight residual densely connected convolutional neural network")

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)