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: "an overview of vulnerabilities of voice controlled systems"
  Abstract: Over the last few years, a rapidly increasing number of Internet-of-Things (IoT) systems that adopt voice as the primary user input have emerged. These systems have been shown to be vulnerable to various types of voice spoofing attacks. However, how exactly these techniques differ or relate to each other has not been extensively studied. In this paper, we provide a survey of recent attack and defense techniques for voice controlled systems and propose a classification of these techniques. We also discuss the need for a universal defense strategy that protects a system from various types of attacks.

Connected through hyperedge HE-1 (induced by source paper "detecting replay attacks using multi channel audio a neural network based method"):
  with members:
    - "remasc realistic replay attack corpus for voice controlled systems"
    - "real time adversarial attacks"
    - "protecting voice controlled systems using sound source identification based on acoustic cues"
    - "the insecurity of home digital voice assistants amazon alexa as a case study"
    - "a study on replay attack and anti spoofing for automatic speaker verification"
    - "decoupled weight decay regularization"
    - "crafting adversarial examples for speech paralinguistics applications"
    - "your voice assistant is mine how to abuse speakers to steal information and control your phone"

Connected through hyperedge HE-2 (induced by source paper "optimization and abstraction a synergistic approach for analyzing neural network robustness"):
  with members:
    - "formal security analysis of neural networks using symbolic intervals"
    - "robust physical world attacks on deep learning models"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "output range analysis for deep neural networks"
    - "towards practical verification of machine learning the case of computer vision systems"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "deepsafe a data driven approach for checking adversarial robustness in neural networks"
    - "robust physical world attacks on machine learning models"

Connected through hyperedge HE-3 (induced by source paper "universal adversarial examples in speech command classification"):
  with members:
    - "universal adversarial audio perturbations"
    - "universal adversarial perturbations for speech recognition systems"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "audio adversarial examples targeted attacks on speech to text"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "continuous authentication for voice assistants"

Connected through hyperedge HE-4 (induced by source paper "real time adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "robust audio adversarial example for a physical attack"
    - "universal adversarial perturbations for speech recognition systems"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial perturbations against real time video classification systems"

Connected through hyperedge HE-5 (induced by source paper "remasc realistic replay attack corpus for voice controlled systems"):
  with members:
    - "real time adversarial attacks"
    - "protecting voice controlled systems using sound source identification based on acoustic cues"
    - "audio adversarial examples targeted attacks on speech to text"
    - "dolphinattack inaudible voice commands"
    - "a study on replay attack and anti spoofing for automatic speaker verification"
    - "crafting adversarial examples for speech paralinguistics applications"
    - "your voice assistant is mine how to abuse speakers to steal information and control your phone"

Connected through hyperedge HE-6 (induced by source paper "protecting voice controlled systems using sound source identification based on acoustic cues"):
  with members:
    - "did you hear that adversarial examples against automatic speech recognition"
    - "audio adversarial examples targeted attacks on speech to text"
    - "continuous authentication for voice assistants"
    - "dolphinattack inaudible voice commands"
    - "the insecurity of home digital voice assistants amazon alexa as a case study"
    - "a study on replay attack and anti spoofing for automatic speaker verification"
    - "crafting adversarial examples for speech paralinguistics applications"
    - "your voice assistant is mine how to abuse speakers to steal information and control your phone"

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.CR(Cryptography and Security)