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: "neural speech synthesis with transformer network"
  Abstract: Although end-to-end neural text-to-speech (TTS) methods (such as Tacotron2) are proposed and achieve state-of-the-art performance, they still suffer from two problems: 1) low efficiency during training and inference; 2) hard to model long dependency using current recurrent neural networks (RNNs). Inspired by the success of Transformer network in neural machine translation (NMT), in this paper, we introduce and adapt the multi-head attention mechanism to replace the RNN structures and also the original attention mechanism in Tacotron2. With the help of multi-head self-attention, the hidden states in the encoder and decoder are constructed in parallel, which improves the training efficiency. Meanwhile, any two inputs at different times are connected directly by self-attention mechanism, which solves the long range dependency problem effectively. Using phoneme sequences as input, our Transformer TTS network generates mel spectrograms, followed by a WaveNet vocoder to output the final audio results. Experiments are conducted to test the efficiency and performance of our new network. For the efficiency, our Transformer TTS network can speed up the training about 4.25 times faster compared with Tacotron2. For the performance, rigorous human tests show that our proposed model achieves state-of-the-art performance (outperforms Tacotron2 with a gap of 0.048) and is very close to human quality (4.39 vs 4.44 in MOS).

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 "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-3 (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"
    - "end to end speech recognition with word based rnn language models"

Connected through hyperedge HE-4 (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-5 (induced by source paper "fastspeech fast robust and controllable text to speech"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "parallel neural text to speech"
    - "waveglow a flow based generative network for speech synthesis"
    - "clarinet parallel wave generation in end to end text to speech"
    - "close to human quality tts with transformer"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "attention is all you need"
    - "parallel wavenet fast high fidelity speech synthesis"

Connected through hyperedge HE-6 (induced by source paper "probing the phonetic and phonological knowledge of tones in mandarin tts models"):
  with members:
    - "linguistic generalization and compositionality in modern artificial neural networks"
    - "using priming to uncover the organization of syntactic representations in neural language models"
    - "fastspeech fast robust and controllable text to speech"
    - "towards transfer learning for end to end speech synthesis from deep pre trained language models"
    - "neural language models as psycholinguistic subjects representations of syntactic state"
    - "representation learning for discovering phonemic tone contours"
    - "dynamic prosody generation for speech synthesis using linguistics driven acoustic embedding selection"
    - "pre training with whole word masking for chinese bert"

Connected through hyperedge HE-7 (induced by source paper "towards transfer learning for end to end speech synthesis from deep pre trained language models"):
  with members:
    - "feature reinforcement with word embedding and parsing information in neural tts"
    - "an unsupervised autoregressive model for speech representation learning"
    - "waveglow a flow based generative network for speech synthesis"
    - "clarinet parallel wave generation in end to end text to speech"
    - "deep contextualized word representations"
    - "universal language model fine tuning for text classification"
    - "semi supervised training for improving data efficiency in end to end speech synthesis"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "attention forcing for sequence to sequence model training"):
  with members:
    - "close to human quality tts with transformer"
    - "efficient neural audio synthesis"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "tacotron towards end to end speech synthesis"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "samplernn an unconditional end to end neural audio generation model"
    - "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.CL(Computation and Language)