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: "protecting voice controlled systems using sound source identification based on acoustic cues"
  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. Existing defense techniques can usually only protect from a specific type of attack or require an additional authentication step that involves another device. Such defense strategies are either not strong enough or lower the usability of the system. Based on the fact that legitimate voice commands should only come from humans rather than a playback device, we propose a novel defense strategy that is able to detect the sound source of a voice command based on its acoustic features. The proposed defense strategy does not require any information other than the voice command itself and can protect a system from multiple types of spoofing attacks. Our proof-of- concept experiments verify the feasibility and effectiveness of this defense strategy.

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"
    - "an overview of vulnerabilities of voice controlled systems"
    - "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 "remasc realistic replay attack corpus for voice controlled systems"):
  with members:
    - "real time adversarial attacks"
    - "audio adversarial examples targeted attacks on speech to text"
    - "an overview of vulnerabilities of voice controlled systems"
    - "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"

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)