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: "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
  Abstract: This paper describes a novel text-to-speech (TTS) technique based on deep convolutional neural networks (CNN), without any recurrent units. Recurrent neural network (RNN) has been a standard technique to model sequential data recently, and this technique has been used in some cutting-edge neural TTS techniques. However, training RNN component often requires a very powerful computer, or very long time typically several days or weeks. Recent other studies, on the other hand, have shown that CNN-based sequence synthesis can be much faster than RNN-based techniques, because of high parallelizability. The objective of this paper is to show an alternative neural TTS system, based only on CNN, that can alleviate these economic costs of training. In our experiment, the proposed Deep Convolutional TTS can be sufficiently trained only in a night (15 hours), using an ordinary gaming PC equipped with two GPUs, while the quality of the synthesized speech was almost acceptable.

Connected through hyperedge HE-1 (induced by source paper "comparison of speech representations for automatic quality estimation in multi speaker text to speech synthesis"):
  with members:
    - "mosnet deep learning based objective assessment for voice conversion"
    - "speech replay detection with x vector attack embeddings and spectral features"
    - "embeddings for dnn speaker adaptive training"
    - "libritts a corpus derived from librispeech for text to speech"
    - "analyzing deep cnn based utterance embeddings for acoustic model adaptation"
    - "quality net an end to end non intrusive speech quality assessment model based on blstm"
    - "automos learning a non intrusive assessor of naturalness of speech"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "moglow probabilistic and controllable motion synthesis using normalising flows"):
  with members:
    - "realistic speech driven facial animation with gans"
    - "analyzing input and output representations for speech driven gesture generation"
    - "generating diverse high fidelity images with vq vae 2"
    - "fixup initialization residual learning without normalization"
    - "videoflow a flow based generative model for video"
    - "waveglow a flow based generative network for speech synthesis"
    - "quaternet a quaternion based recurrent model for human motion"
    - "flowavenet a generative flow for raw audio"

Connected through hyperedge HE-3 (induced by source paper "deep long audio inpainting"):
  with members:
    - "deep video inpainting"
    - "adversarial generation of time frequency features with application in audio synthesis"
    - "free form video inpainting with 3d gated convolution and temporal patchgan"
    - "learnable gated temporal shift module for deep video inpainting"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "image inpainting for irregular holes using partial convolutions"
    - "a context encoder for audio inpainting"
    - "efficient neural audio synthesis"

Connected through hyperedge HE-4 (induced by source paper "a comparative study on transformer vs rnn in speech applications"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "language modeling with deep transformers"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "ted lium 3 twice as much data and corpus repartition for experiments on speaker adaptation"
    - "scaling neural machine translation"
    - "training tips for the transformer model"
    - "neural speech synthesis with transformer network"

Connected through hyperedge HE-5 (induced by source paper "towards automatic face to face translation"):
  with members:
    - "deep audio visual speech recognition"
    - "a baseline neural machine translation system for indian languages"
    - "massively multilingual neural machine translation"
    - "neural voice cloning with a few samples"
    - "lip movements generation at a glance"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "rapid adaptation of neural machine translation to new languages"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "espnet tts unified reproducible and integratable open source end to end text to speech toolkit"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "jvs corpus free japanese multi speaker voice corpus"
    - "libritts a corpus derived from librispeech for text to speech"
    - "mixed precision training for nlp and speech recognition with openseq2seq"
    - "back translation style data augmentation for end to end asr"
    - "waveglow a flow based generative network for speech synthesis"
    - "espnet end to end speech processing toolkit"
    - "close to human quality tts with transformer"

Connected through hyperedge HE-7 (induced by source paper "jejueo datasets for machine translation and speech synthesis"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "css10 a collection of single speaker speech datasets for 10 languages"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "universal neural machine translation for extremely low resource languages"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "attention is all you need"
    - "tacotron towards end to end speech synthesis"

Connected through hyperedge HE-8 (induced by source paper "sequence to sequence singing synthesis using the feed forward transformer"):
  with members:
    - "singing voice synthesis using deep autoregressive neural networks for acoustic modeling"
    - "fastspeech fast robust and controllable text to speech"
    - "parallel neural text to speech"
    - "wgansing a multi voice singing voice synthesizer based on the wasserstein gan"
    - "adversarially trained end to end korean singing voice synthesis system"
    - "waveglow a flow based generative network for speech synthesis"
    - "neural speech synthesis with transformer network"
    - "self attentional acoustic models"

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)