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: "voice impersonation using generative adversarial networks"
  Abstract: Voice impersonation is not the same as voice transformation, although the latter is an essential element of it. In voice impersonation, the resultant voice must convincingly convey the impression of having been naturally produced by the target speaker, mimicking not only the pitch and other perceivable signal qualities, but also the style of the target speaker. In this paper, we propose a novel neural network based speech quality- and style- mimicry framework for the synthesis of impersonated voices. The framework is built upon a fast and accurate generative adversarial network model. Given spectrographic representations of source and target speakers' voices, the model learns to mimic the target speaker's voice quality and style, regardless of the linguistic content of either's voice, generating a synthetic spectrogram from which the time domain signal is reconstructed using the Griffin-Lim method. In effect, this model reframes the well-known problem of style-transfer for images as the problem of style-transfer for speech signals, while intrinsically addressing the problem of durational variability of speech sounds. Experiments demonstrate that the model can generate extremely convincing samples of impersonated speech. It is even able to impersonate voices across different genders effectively. Results are qualitatively evaluated using standard procedures for evaluating synthesized voices.

Connected through hyperedge HE-1 (induced by source paper "probabilistic forecasting of sensory data with generative adversarial networks forgan"):
  with members:
    - "phaselink a deep learning approach to seismic phase association"
    - "predicting inpatient discharge prioritization with electronic health records"
    - "deepdownscale a deep learning strategy for high resolution weather forecast"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "cred a deep residual network of convolutional and recurrent units for earthquake signal detection"
    - "svsgan singing voice separation via generative adversarial network"
    - "adversarial learning for neural dialogue generation"
    - "model free renewable scenario generation using generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "unsupervised end to end learning of discrete linguistic units for voice conversion"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "decoupled learning for conditional adversarial networks"
    - "improved audio embeddings by adjacency based clustering with applications in spoken term detection"
    - "unsupervised learning of semantic audio representations"
    - "tacotron towards end to end speech synthesis"
    - "improved training of wasserstein gans"
    - "deep voice 3 2000 speaker neural text to speech"

Connected through hyperedge HE-3 (induced by source paper "deep music analogy via latent representation disentanglement"):
  with members:
    - "inspecting and interacting with meaningful music representations using vae"
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "isolating sources of disentanglement in variational autoencoders"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "disentangled sequential autoencoder"
    - "cover song synthesis by analogy"
    - "music style transfer a position paper"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "the zero resource speech challenge 2019 tts without t"):
  with members:
    - "unsupervised acoustic unit discovery for speech synthesis using discrete latent variable neural networks"
    - "vqvae unsupervised unit discovery and multi scale code2spec inverter for zerospeech challenge 2019"
    - "unsupervised end to end learning of discrete linguistic units for voice conversion"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "close to human quality tts with transformer"
    - "bayesian models for unit discovery on a very low resource language"
    - "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop"
    - "listening while speaking speech chain by deep learning"

Connected through hyperedge HE-5 (induced by source paper "on the veracity of cyber intrusion alerts synthesized by generative adversarial networks"):
  with members:
    - "tiresias predicting security events through deep learning"
    - "a style based generator architecture for generative adversarial networks"
    - "idsgan generative adversarial networks for attack generation against intrusion detection"
    - "mine mutual information neural estimation"
    - "black box attacks against rnn based malware detection algorithms"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "rnn based early cyber attack detection for the tennessee eastman process"

Connected through hyperedge HE-6 (induced by source paper "a unified neural architecture for instrumental audio tasks"):
  with members:
    - "audio super resolution using neural networks"
    - "segan speech enhancement generative adversarial network"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "image to image translation with conditional adversarial networks"
    - "wavenet a generative model for raw audio"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "adversarial training in affective computing and sentiment analysis recent advances and perspectives"):
  with members:
    - "geometry contrastive gan for facial expression transfer"
    - "generative adversarial networks an overview"
    - "towards open set identity preserving face synthesis"
    - "talking face generation by conditional recurrent adversarial network"
    - "generative adversarial talking head bringing portraits to life with a weakly supervised neural network"
    - "interactive generative adversarial networks for facial expression generation in dyadic interactions"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "correlated discrete data generation using adversarial training"

Connected through hyperedge HE-8 (induced by source paper "rhythm flexible voice conversion without parallel data using cycle gan over phoneme posteriorgram sequences"):
  with members:
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "voice conversion using sequence to sequence learning of context posterior probabilities"
    - "tacotron towards end to end speech synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "fully character level neural machine translation without explicit segmentation"
    - "wavenet a generative model for raw audio"

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)