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 "sirenattack generating adversarial audio for end to end acoustic systems"):
  Abstract: Despite their immense popularity, deep learning-based acoustic systems are inherently vulnerable to adversarial attacks, wherein maliciously crafted audios trigger target systems to misbehave. In this paper, we present SirenAttack, a new class of attacks to generate adversarial audios. Compared with existing attacks, SirenAttack highlights with a set of significant features: (i) versatile -- it is able to deceive a range of end-to-end acoustic systems under both white-box and black-box settings; (ii) effective -- it is able to generate adversarial audios that can be recognized as specific phrases by target acoustic systems; and (iii) stealthy -- it is able to generate adversarial audios indistinguishable from their benign counterparts to human perception. We empirically evaluate SirenAttack on a set of state-of-the-art deep learning-based acoustic systems (including speech command recognition, speaker recognition and sound event classification), with results showing the versatility, effectiveness, and stealthiness of SirenAttack. For instance, it achieves 99.45% attack success rate on the IEMOCAP dataset against the ResNet18 model, while the generated adversarial audios are also misinterpreted by multiple popular ASR platforms, including Google Cloud Speech, Microsoft Bing Voice, and IBM Speech-to-Text. We further evaluate three potential defense methods to mitigate such attacks, including adversarial training, audio downsampling, and moving average filtering, which leads to promising directions for further research.

Includes members:
  Member "commandersong a systematic approach for practical adversarial voice recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "security vetting process of smart home assistant applications a first look and case studies")
  Member "audio adversarial examples targeted attacks on speech to text":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks")
  Member "continuous authentication for voice assistants":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "short range audio channels security survey of mechanisms applications and research challenges")
  Member "dolphinattack inaudible voice commands":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ghostimage perception domain attacks against vision based object classification systems")
  Member "the insecurity of home digital voice assistants amazon alexa as a case study":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "detecting replay attacks using multi channel audio a neural network based method")
  Member "dual path networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional networks with dense connectivity")
  Member "exploring neural transducers for end to end speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the architectural implications of facebook s dnn based personalized recommendation")
  Member "crafting adversarial examples for speech paralinguistics applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "identifying audio adversarial examples via anomalous pattern detection")

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.CR(Cryptography and Security)