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: "a multiversion programming inspired approach to detecting audio adversarial examples"
  Abstract: Adversarial examples (AEs) are crafted by adding human-imperceptible perturbations to inputs such that a machine-learning based classifier incorrectly labels them. They have become a severe threat to the trustworthiness of machine learning. While AEs in the image domain have been well studied, audio AEs are less investigated. Recently, multiple techniques are proposed to generate audio AEs, which makes countermeasures against them an urgent task. Our experiments show that, given an AE, the transcription results by different Automatic Speech Recognition (ASR) systems differ significantly, as they use different architectures, parameters, and training datasets. Inspired by Multiversion Programming, we propose a novel audio AE detection approach, which utilizes multiple off-the-shelf ASR systems to determine whether an audio input is an AE. The evaluation shows that the detection achieves accuracies over 98.6%.

Connected through hyperedge HE-1 (induced by source paper "dopa a comprehensive cnn detection methodology against physical adversarial attacks"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "simple physical adversarial examples against end to end autonomous driving models"
    - "adversarial patch"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "local gradients smoothing defense against localized adversarial attacks"
    - "characterizing audio adversarial examples using temporal dependency"
    - "robust physical world attacks on deep learning models"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-2 (induced by source paper "lance a comprehensive and lightweight cnn defense methodology against physical adversarial attacks on embedded multimedia applications"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "noise flooding for detecting audio adversarial examples against automatic speech recognition"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "adversarial patch"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "local gradients smoothing defense against localized adversarial attacks"
    - "characterizing audio adversarial examples using temporal dependency"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-3 (induced by source paper "privacy leakage in smart homes and its mitigation ifttt as a case study"):
  with members:
    - "sensitive information tracking in commodity iot"
    - "a smart home is no castle privacy vulnerabilities of encrypted iot traffic"
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"

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.SD(Sound)