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: "deep voice 3 scaling text to speech with convolutional sequence learning"
  Abstract: We present Deep Voice 3, a fully-convolutional attention-based neural text-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training ten times faster. We scale Deep Voice 3 to data set sizes unprecedented for TTS, training on more than eight hundred hours of audio from over two thousand speakers. In addition, we identify common error modes of attention-based speech synthesis networks, demonstrate how to mitigate them, and compare several different waveform synthesis methods. We also describe how to scale inference to ten million queries per day on one single-GPU server.

Connected through hyperedge HE-1 (induced by source paper "semi supervised neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "neural predictor for neural architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "fastspeech fast robust and controllable text to speech"
    - "the evolved transformer"
    - "single path one shot neural architecture search with uniform sampling"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"

Connected through hyperedge HE-2 (induced by source paper "speech2phone a multilingual and text independent speaker identification model"):
  with members:
    - "neural voice cloning with a few samples"
    - "deep speaker an end to end neural speaker embedding system"
    - "deep voice 3 2000 speaker neural text to speech"
    - "tristounet triplet loss for speaker turn embedding"
    - "tf learn tensorflow s high level module for distributed machine learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "waveflow a compact flow based model for raw audio"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "parallel neural text to speech"
    - "high fidelity speech synthesis with adversarial networks"
    - "discrete flows invertible generative models of discrete data"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "emerging convolutions for generative normalizing flows"
    - "blow a single scale hyperconditioned flow for non parallel raw audio voice conversion"
    - "the challenge of realistic music generation modelling raw audio at scale"

Connected through hyperedge HE-4 (induced by source paper "parallel neural text to speech"):
  with members:
    - "fast spectrogram inversion using multi head convolutional neural networks"
    - "waveglow a flow based generative network for speech synthesis"
    - "neural voice cloning with a few samples"
    - "clarinet parallel wave generation in end to end text to speech"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "efficient neural audio synthesis"
    - "stochastic video generation with a learned prior"

Connected through hyperedge HE-5 (induced by source paper "a prior of a googol gaussians a tensor ring induced prior for generative models"):
  with members:
    - "gaussian mixture generative adversarial networks for diverse datasets and the unsupervised clustering of images"
    - "spectral normalization for generative adversarial networks"
    - "latent dirichlet allocation in generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "deep learning and quantum entanglement fundamental connections with implications to network design"
    - "compressing recurrent neural network with tensor train"

Connected through hyperedge HE-6 (induced by source paper "semi supervised generative modeling for controllable speech synthesis"):
  with members:
    - "effective use of variational embedding capacity in expressive end to end speech synthesis"
    - "fastspeech fast robust and controllable text to speech"
    - "chive varying prosody in speech synthesis with a linguistically driven dynamic hierarchical conditional variational network"
    - "efficient neural audio synthesis"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"

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"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "attention is all you need"
    - "tacotron towards end to end speech synthesis"

Connected through hyperedge HE-8 (induced by source paper "in other news a bi style text to speech model for synthesizing newscaster voice with limited data"):
  with members:
    - "predicting expressive speaking style from text in end to end speech synthesis"
    - "neural voice cloning with a few samples"
    - "speaking style adaptation in text to speech synthesis using sequence to sequence models with attention"
    - "effect of data reduction on sequence to sequence neural tts"
    - "close to human quality tts with transformer"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"

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)