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: "semi supervised training for improving data efficiency in end to end speech synthesis"
  Abstract: Although end-to-end text-to-speech (TTS) models such as Tacotron have shown excellent results, they typically require a sizable set of high-quality   pairs for training, which are expensive to collect. In this paper, we propose a semi-supervised training framework to improve the data efficiency of Tacotron. The idea is to allow Tacotron to utilize textual and acoustic knowledge contained in large, publicly-available text and speech corpora. Importantly, these external data are unpaired and potentially noisy. Specifically, first we embed each word in the input text into word vectors and condition the Tacotron encoder on them. We then use an unpaired speech corpus to pre-train the Tacotron decoder in the acoustic domain. Finally, we fine-tune the model using available paired data. We demonstrate that the proposed framework enables Tacotron to generate intelligible speech using less than half an hour of paired training data.

Connected through hyperedge HE-1 (induced by source paper "an unsupervised autoregressive model for speech representation learning"):
  with members:
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "a theoretical analysis of contrastive unsupervised representation learning"
    - "unspeech unsupervised speech context embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "dissecting contextual word embeddings architecture and representation"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-2 (induced by source paper "learning to groove with inverse sequence transformations"):
  with members:
    - "counterpoint by convolution"
    - "video to video synthesis"
    - "predicting expressive speaking style from text in end to end speech synthesis"
    - "this time with feeling learning expressive musical performance"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "hierarchical neural story generation"
    - "a hierarchical latent vector model for learning long term structure in music"

Connected through hyperedge HE-3 (induced by source paper "independent language modeling architecture for end to end asr"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "espnet end to end speech processing toolkit"
    - "multi modal data augmentation for end to end asr"
    - "end to end speech recognition with word based rnn language models"
    - "catastrophic forgetting still a problem for dnns"
    - "state of the art speech recognition with sequence to sequence models"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "towards better decoding and language model integration in sequence to sequence models"

Connected through hyperedge HE-4 (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"
    - "neural speech synthesis with transformer network"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "libritts a corpus derived from librispeech for text to speech"):
  with members:
    - "predicting expressive speaking style from text in end to end speech synthesis"
    - "neural voice cloning with a few samples"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "efficient neural audio synthesis"
    - "representation mixing for tts synthesis"
    - "sample efficient adaptive text to speech"
    - "neural speech synthesis with transformer network"
    - "hierarchical generative modeling for controllable speech synthesis"

Connected through hyperedge HE-6 (induced by source paper "adversarially trained end to end korean singing voice synthesis system"):
  with members:
    - "cgans with projection discriminator"
    - "spectral normalization for generative adversarial networks"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "large scale gan training for high fidelity natural image synthesis"
    - "which training methods for gans do actually converge"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "tacotron towards end to end speech synthesis"

Connected through hyperedge HE-7 (induced by source paper "teacher student training for robust tacotron based tts"):
  with members:
    - "robust sequence to sequence acoustic modeling with stepwise monotonic attention for neural tts"
    - "fastspeech fast robust and controllable text to speech"
    - "a new gan based end to end tts training algorithm"
    - "generalization in generation a closer look at exposure bias"
    - "robust and fine grained prosody control of end to end speech synthesis"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "online and linear time attention by enforcing monotonic alignments"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-8 (induced by source paper "end to end text to speech for low resource languages by cross lingual transfer learning"):
  with members:
    - "neural voice cloning with a few samples"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "a study of all convolutional encoders for connectionist temporal classification"
    - "tacotron towards end to end speech synthesis"
    - "deep voice 3 2000 speaker neural text to speech"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"

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)