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: "your voice assistant is mine how to abuse speakers to steal information and control your phone"
  Abstract: Previous research about sensor based attacks on Android platform focused mainly on accessing or controlling over sensitive device components, such as camera, microphone and GPS. These approaches get data from sensors directly and need corresponding sensor invoking permissions. #R##N#This paper presents a novel approach (GVS-Attack) to launch permission bypassing attacks from a zero permission Android application (VoicEmployer) through the speaker. The idea of GVS-Attack utilizes an Android system built-in voice assistant module -- Google Voice Search. Through Android Intent mechanism, VoicEmployer triggers Google Voice Search to the foreground, and then plays prepared audio files (like "call number 1234 5678") in the background. Google Voice Search can recognize this voice command and execute corresponding operations. With ingenious designs, our GVS-Attack can forge SMS/Email, access privacy information, transmit sensitive data and achieve remote control without any permission. #R##N#Also we found a vulnerability of status checking in Google Search app, which can be utilized by GVS-Attack to dial arbitrary numbers even when the phone is securely locked with password. A prototype of VoicEmployer has been implemented to demonstrate the feasibility of GVS-Attack in real world. In theory, nearly all Android devices equipped with Google Services Framework can be affected by GVS-Attack. This study may inspire application developers and researchers rethink that zero permission doesn't mean safety and the speaker can be treated as a new attack surface.

Connected through hyperedge HE-1 (induced by source paper "short range audio channels security survey of mechanisms applications and research challenges"):
  with members:
    - "perils of zero interaction security in the internet of things"
    - "a survey on context based co presence detection techniques"
    - "doubleecho mitigating context manipulation attacks in copresence verification"
    - "survey and systematization of secure device pairing"
    - "continuous authentication for voice assistants"
    - "dolphinattack inaudible voice commands"
    - "a survey on homomorphic encryption schemes theory and implementation"
    - "don t skype type acoustic eavesdropping in voice over ip"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "meet malexa alexa s malicious twin malware induced misperception through intelligent voice assistants"):
  with members:
    - "user perceptions of smart home iot privacy"

Connected through hyperedge HE-4 (induced by source paper "sirenattack generating adversarial audio for end to end acoustic systems"):
  with members:
    - "commandersong a systematic approach for practical adversarial voice 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"
    - "dual path networks"
    - "exploring neural transducers for end to end speech recognition"
    - "crafting adversarial examples for speech paralinguistics applications"

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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "a context aware framework for detecting sensor based threats on smart devices"):
  with members:
    - "sensitive information tracking in commodity iot"
    - "a survey on sensor based threats to internet of things iot devices and applications"
    - "iotdots a digital forensics framework for smart environments"
    - "pin skimming exploiting the ambient light sensor in mobile devices"

Connected through hyperedge HE-7 (induced by source paper "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"):
  with members:
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "black box adversarial attacks with limited queries and information"
    - "stealing hyperparameters in machine learning"
    - "dolphinattack inaudible voice commands"
    - "english conversational telephone speech recognition by humans and machines"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "inaudible voice commands"
    - "a network of deep neural networks for distant speech recognition"

Connected through hyperedge HE-8 (induced by source paper "commandersong a systematic approach for practical adversarial voice recognition"):
  with members:
    - "bebp an poisoning method against machine learning based idss"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "audio adversarial examples targeted attacks on speech to text"
    - "robust physical world attacks on deep learning models"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "wav2letter an end to end convnet based speech recognition system"
    - "adversarial example defenses ensembles of weak defenses are not strong"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"

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)