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: "attacking speaker recognition with deep generative models"
  Abstract: In this paper we investigate the ability of generative adversarial networks (GANs) to synthesize spoofing attacks on modern speaker recognition systems. We first show that samples generated with SampleRNN and WaveNet are unable to fool a CNN-based speaker recognition system. We propose a modification of the Wasserstein GAN objective function to make use of data that is real but not from the class being learned. Our semi-supervised learning method is able to perform both targeted and untargeted attacks, raising questions related to security in speaker authentication systems.

Connected through hyperedge HE-1 (induced by source paper "hear no evil see kenansville efficient and transferable black box attacks on speech recognition and voice identification systems"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "practical hidden voice attacks against speech and speaker recognition systems"
    - "targeted adversarial examples for black box audio systems"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "adversarial patch"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "characterizing audio adversarial examples using temporal dependency"

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"
    - "fooling end to end speaker verification by adversarial examples"
    - "voice conversion using sequence to sequence learning of context posterior probabilities"
    - "a study on replay attack and anti spoofing for automatic speaker verification"
    - "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 "learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "pacgan the power of two samples in generative adversarial networks"
    - "hierarchical video generation from orthogonal information optical flow and texture"
    - "stabilizing adversarial nets with prediction methods"
    - "mocogan decomposing motion and content for video generation"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "pr epsilon epsilon ch a system for privacy preserving speech transcription"):
  with members:
    - "privacy preserving adversarial representation learning in asr reality or illusion"
    - "the audio auditor participant level membership inference in internet of things voice services"
    - "emotionless privacy preserving speech analysis for voice assistants"
    - "shrinkwrap differentially private query processing in private data federations"
    - "voicemask anonymize and sanitize voice input on mobile devices"
    - "tacotron towards end to end speech synthesis"
    - "wavenet a generative model for raw audio"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-5 (induced by source paper "spreech a system for privacy preserving speech transcription"):
  with members:
    - "privacy preserving adversarial representation learning in asr reality or illusion"
    - "emotionless privacy preserving speech analysis for voice assistants"
    - "shrinkwrap differentially private query processing in private data federations"
    - "voicemask anonymize and sanitize voice input on mobile devices"
    - "deep speech scaling up end to end speech recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "tequilagan how to easily identify gan samples"):
  with members:
    - "improved training of wasserstein gans"
    - "energy based generative adversarial network"
    - "improved techniques for training gans"
    - "least squares generative adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

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)