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: "parallel neural text to speech"
  Abstract: In this work, we propose a non-autoregressive seq2seq model that converts text to spectrogram. It is fully convolutional and obtains about 46.7 times speed-up over Deep Voice 3 at synthesis while maintaining comparable speech quality using a WaveNet vocoder. Interestingly, it has even fewer attention errors than the autoregressive model on the challenging test sentences. Furthermore, we build the first fully parallel neural text-to-speech system by applying the inverse autoregressive flow~(IAF) as the parallel neural vocoder. Our system can synthesize speech from text through a single feed-forward pass. We also explore a novel approach to train the IAF from scratch as a generative model for raw waveform, which avoids the need for distillation from a separately trained WaveNet.

Connected through hyperedge HE-1 (induced by source paper "waveflow a compact flow based model for raw audio"):
  with members:
    - "fastspeech fast robust and controllable 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"
    - "waveglow a flow based generative network for speech synthesis"

Connected through hyperedge HE-2 (induced by source paper "self supervised speech recognition via local prior matching"):
  with members:
    - "attentive adversarial learning for domain invariant training"
    - "libri light a benchmark for asr with limited or no supervision"
    - "lessons from building acoustic models with a million hours of speech"
    - "a fully differentiable beam search decoder"
    - "back translation style data augmentation for end to end asr"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "cycle consistency training for end to end speech recognition"
    - "end to end feedback loss in speech chain framework via straight through estimator"

Connected through hyperedge HE-3 (induced by source paper "fastspeech fast robust and controllable text to speech"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "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"
    - "neural speech synthesis with transformer network"
    - "attention is all you need"
    - "parallel wavenet fast high fidelity speech synthesis"

Connected through hyperedge HE-4 (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"
    - "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"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"

Connected through hyperedge HE-5 (induced by source paper "multi speaker end to end speech synthesis"):
  with members:
    - "libritts a corpus derived from librispeech for text to speech"
    - "neural voice cloning with a few samples"
    - "clarinet parallel wave generation in end to end text to speech"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "deep voice real time neural text to speech"
    - "tacotron towards end to end speech synthesis"
    - "deep voice 3 2000 speaker neural text to speech"
    - "deep voice 2 multi speaker neural text to speech"

Connected through hyperedge HE-6 (induced by source paper "incremental text to speech synthesis with prefix to prefix framework"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "waveglow a flow based generative network for speech synthesis"
    - "neural speech synthesis with transformer network"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "deep voice 2 multi speaker neural text to speech"
    - "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)