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 study on replay attack and anti spoofing for automatic speaker verification"
  Abstract: For practical automatic speaker verification (ASV) systems, replay attack poses a true risk. By replaying a pre-recorded speech signal of the genuine speaker, ASV systems tend to be easily fooled. An effective replay detection method is therefore highly desirable. In this study, we investigate a major difficulty in replay detection: the over-fitting problem caused by variability factors in speech signal. An F-ratio probing tool is proposed and three variability factors are investigated using this tool: speaker identity, speech content and playback & recording device. The analysis shows that device is the most influential factor that contributes the highest over-fitting risk. A frequency warping approach is studied to alleviate the over-fitting problem, as verified on the ASV-spoof 2017 database.

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"
    - "an overview of vulnerabilities of voice controlled systems"
    - "the insecurity of home digital voice assistants amazon alexa as a case study"
    - "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 "introduction to voice presentation attack detection and recent advances"):
  with members:
    - "generalization of spoofing countermeasures a case study with asvspoof 2015 and btas 2016 corpora"
    - "attacking speaker recognition with deep generative models"
    - "fooling end to end speaker verification by adversarial examples"
    - "voice conversion using sequence to sequence learning of context posterior probabilities"
    - "tacotron towards end to end speech synthesis"
    - "deep voice 2 multi speaker neural text to speech"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "statistical parametric speech synthesis incorporating generative adversarial networks"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "towards robust audio spoofing detection a detailed comparison of traditional and learned features"):
  with members:
    - "singing voice separation using a deep convolutional neural network trained by ideal binary mask and cross entropy"
    - "utd crss systems for 2016 nist speaker recognition evaluation"
    - "learning from synthetic data using a stacked multichannel autoencoder"
    - "voice recognition algorithms using mel frequency cepstral coefficient mfcc and dynamic time warping dtw techniques"

Connected through hyperedge HE-5 (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"
    - "an overview of vulnerabilities of voice controlled systems"
    - "continuous authentication for voice assistants"
    - "dolphinattack inaudible voice commands"
    - "the insecurity of home digital voice assistants amazon alexa as a case study"
    - "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.SD(Sound)