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 2 multi speaker neural text to speech"
  Abstract: We introduce a technique for augmenting neural text-to-speech (TTS) with lowdimensional trainable speaker embeddings to generate different voices from a single model. As a starting point, we show improvements over the two state-ofthe-art approaches for single-speaker neural TTS: Deep Voice 1 and Tacotron. We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1. We improve Tacotron by introducing a post-processing neural vocoder, and demonstrate a significant audio quality improvement. We then demonstrate our technique for multi-speaker speech synthesis for both Deep Voice 2 and Tacotron on two multi-speaker TTS datasets. We show that a single neural TTS system can learn hundreds of unique voices from less than half an hour of data per speaker, while achieving high audio quality synthesis and preserving the speaker identities almost perfectly.

Connected through hyperedge HE-1 (induced by source paper "unpaired image to speech synthesis with multimodal information bottleneck"):
  with members:
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "fitting new speakers based on a short untranscribed sample"
    - "recurrent fusion network for image captioning"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "letter based speech recognition with gated convnets"
    - "attention is all you need"
    - "improving end to end speech recognition with policy learning"

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 "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-4 (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-5 (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"
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "attention is all you need"

Connected through hyperedge HE-6 (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"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "statistical parametric speech synthesis incorporating generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "expediting tts synthesis with adversarial vocoding"):
  with members:
    - "adversarial audio synthesis"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "segan speech enhancement generative adversarial network"
    - "deep voice real time neural text to speech"
    - "tacotron towards end to end speech synthesis"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "tts skins speaker conversion via asr"):
  with members:
    - "mixed precision training for nlp and speech recognition with openseq2seq"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "sample efficient adaptive text to speech"
    - "fitting new speakers based on a short untranscribed sample"
    - "a universal music translation network"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "wav2letter an end to end convnet based speech recognition system"

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)