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: "statistical parametric speech synthesis incorporating generative adversarial networks"
  Abstract: A method for statistical parametric speech synthesis incorporating generative adversarial networks (GANs) is proposed. Although powerful deep neural networks (DNNs) techniques can be applied to artificially synthesize speech waveform, the synthetic speech quality is low compared with that of natural speech. One of the issues causing the quality degradation is an over-smoothing effect often observed in the generated speech parameters. A GAN introduced in this paper consists of two neural networks: a discriminator to distinguish natural and generated samples, and a generator to deceive the discriminator. In the proposed framework incorporating the GANs, the discriminator is trained to distinguish natural and generated speech parameters, while the acoustic models are trained to minimize the weighted sum of the conventional minimum generation loss and an adversarial loss for deceiving the discriminator. Since the objective of the GANs is to minimize the divergence (i.e., distribution difference) between the natural and generated speech parameters, the proposed method effectively alleviates the over-smoothing effect on the generated speech parameters. We evaluated the effectiveness for text-to-speech and voice conversion, and found that the proposed method can generate more natural spectral parameters and $F_0$ than conventional minimum generation error training algorithm regardless its hyper-parameter settings. Furthermore, we investigated the effect of the divergence of various GANs, and found that a Wasserstein GAN minimizing the Earth-Mover's distance works the best in terms of improving synthetic speech quality.

Connected through hyperedge HE-1 (induced by source paper "a novel framework for selection of gans for an application"):
  with members:
    - "a review on generative adversarial networks algorithms theory and applications"
    - "the six fronts of the generative adversarial networks"
    - "generative adversarial networks a survey and taxonomy"
    - "stabilizing generative adversarial network training a survey"
    - "how generative adversarial networks and their variants work an overview"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "improving generalization and stability of generative adversarial networks"
    - "adversarial video generation on complex datasets"

Connected through hyperedge HE-2 (induced by source paper "high fidelity speech synthesis with adversarial networks"):
  with members:
    - "large scale adversarial representation learning"
    - "adversarial video generation on complex datasets"
    - "gansynth adversarial neural audio synthesis"
    - "expediting tts synthesis with adversarial vocoding"
    - "tganv2 efficient training of large models for video generation with multiple subsampling layers"
    - "waveglow a flow based generative network for speech synthesis"
    - "efficient neural audio synthesis"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "earthquakegen earthquake simulation using generative adversarial networks"):
  with members:
    - "deepdetect a cascaded region based densely connected network for seismic event detection"
    - "gansynth adversarial neural audio synthesis"
    - "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "spectral normalization for generative adversarial networks"
    - "generative adversarial networks an overview"
    - "large scale gan training for high fidelity natural image synthesis"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "wavecyclegan2 time domain neural post filter for speech waveform generation"):
  with members:
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "waveglow a flow based generative network for speech synthesis"
    - "clarinet parallel wave generation in end to end text to speech"
    - "efficient neural audio synthesis"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "vqvae unsupervised unit discovery and multi scale code2spec inverter for zerospeech challenge 2019"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "machine speech chain with one shot speaker adaptation"
    - "end to end feedback loss in speech chain framework via straight through estimator"
    - "listening while speaking speech chain by deep learning"
    - "the zero resource speech challenge 2017"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "neural discrete representation learning"

Connected through hyperedge HE-7 (induced by source paper "a new gan based end to end tts training algorithm"):
  with members:
    - "clarinet parallel wave generation in end to end text to speech"
    - "close to human quality tts with transformer"
    - "domain adversarial training for accented speech recognition"
    - "efficient neural audio synthesis"
    - "spectral normalization for generative adversarial networks"
    - "attention is all you need"
    - "statistical parametric speech synthesis using generative adversarial networks under a multi task learning framework"
    - "tacotron towards end to end speech synthesis"

Connected through hyperedge HE-8 (induced by source paper "taco vc a single speaker tacotron based voice conversion with limited data"):
  with members:
    - "neural voice cloning with a few samples"
    - "voice conversion with conditional samplernn"
    - "fitting new speakers based on a short untranscribed sample"
    - "voice conversion using sequence to sequence learning of context posterior probabilities"
    - "tacotron towards end to end speech synthesis"
    - "a wavenet for speech denoising"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "towards end to end speech recognition with deep convolutional neural 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)