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: "machine speech chain with one shot speaker adaptation"
  Abstract: In previous work, we developed a closed-loop speech chain model based on deep learning, in which the architecture enabled the automatic speech recognition (ASR) and text-to-speech synthesis (TTS) components to mutually improve their performance. This was accomplished by the two parts teaching each other using both labeled and unlabeled data. This approach could significantly improve model performance within a single-speaker speech dataset, but only a slight increase could be gained in multi-speaker tasks. Furthermore, the model is still unable to handle unseen speakers. In this paper, we present a new speech chain mechanism by integrating a speaker recognition model inside the loop. We also propose extending the capability of TTS to handle unseen speakers by implementing one-shot speaker adaptation. This enables TTS to mimic voice characteristics from one speaker to another with only a one-shot speaker sample, even from a text without any speaker information. In the speech chain loop mechanism, ASR also benefits from the ability to further learn an arbitrary speaker's characteristics from the generated speech waveform, resulting in a significant improvement in the recognition rate.

Connected through hyperedge HE-1 (induced by source paper "from speech chain to multimodal chain leveraging cross modal data augmentation for semi supervised learning"):
  with members:
    - "deep audio visual speech recognition"
    - "towards robust neural machine translation"
    - "multi scale alignment and contextual history for attention mechanism in sequence to sequence model"
    - "turbo learning for captionbot and drawingbot"
    - "end to end feedback loss in speech chain framework via straight through estimator"
    - "english conversational telephone speech recognition by humans and machines"
    - "the microsoft 2017 conversational speech recognition system"
    - "end to end audiovisual fusion with lstms"

Connected through hyperedge HE-2 (induced by source paper "speech to speech translation between untranscribed unknown languages"):
  with members:
    - "vqvae unsupervised unit discovery and multi scale code2spec inverter for zerospeech challenge 2019"
    - "unsupervised end to end learning of discrete linguistic units for voice conversion"
    - "the zero resource speech challenge 2019 tts without t"
    - "direct speech to speech translation with a sequence to sequence model"
    - "multi scale alignment and contextual history for attention mechanism in sequence to sequence model"
    - "end to end feedback loss in speech chain framework via straight through estimator"
    - "sequence to sequence models can directly translate foreign speech"
    - "listening while speaking speech chain by deep learning"

Connected through hyperedge HE-3 (induced by source paper "generating synthetic audio data for attention based speech recognition systems"):
  with members:
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "language modeling with deep transformers"
    - "libritts a corpus derived from librispeech for text to speech"
    - "cumulative adaptation for blstm acoustic models"
    - "back translation style data augmentation for end to end asr"
    - "end to end feedback loss in speech chain framework via straight through estimator"
    - "training neural speech recognition systems with synthetic speech augmentation"
    - "improved training of end to end attention models for speech recognition"

Connected through hyperedge HE-4 (induced by source paper "vqvae unsupervised unit discovery and multi scale code2spec inverter for zerospeech challenge 2019"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "end to end feedback loss in speech chain framework via straight through estimator"
    - "listening while speaking speech chain by deep learning"
    - "the zero resource speech challenge 2017"
    - "statistical parametric speech synthesis incorporating generative adversarial networks"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "neural discrete representation learning"

Connected through hyperedge HE-5 (induced by source paper "one shot voice conversion by separating speaker and content representations with instance normalization"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "rhythm flexible voice conversion without parallel data using cycle gan over phoneme posteriorgram sequences"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "voice conversion using sequence to sequence learning of context posterior probabilities"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "tacotron towards end to end speech synthesis"

Connected through hyperedge HE-6 (induced by source paper "speech recognition with augmented synthesized speech"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "efficient neural audio synthesis"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "training neural speech recognition systems with synthetic speech augmentation"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "state of the art speech recognition with sequence to sequence models"
    - "listening while speaking speech chain by deep learning"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"

Connected through hyperedge HE-7 (induced by source paper "leveraging weakly supervised data to improve end to end speech to text translation"):
  with members:
    - "back translation style data augmentation for end to end asr"
    - "tied multitask learning for neural speech translation"
    - "end to end automatic speech translation of audiobooks"
    - "multi modal data augmentation for end to end asr"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "structured based curriculum learning for end to end english japanese speech translation"
    - "hierarchical generative modeling for controllable speech synthesis"

Connected through hyperedge HE-8 (induced by source paper "multi scale alignment and contextual history for attention mechanism in sequence to sequence model"):
  with members:
    - "online and linear time attention by enforcing monotonic alignments"
    - "listening while speaking speech chain by deep learning"
    - "tacotron towards end to end speech synthesis"
    - "attention based wav2text with feature transfer learning"
    - "local monotonic attention mechanism for end to end speech and language processing"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "effective approaches to attention based neural machine translation"
    - "end to end attention based large vocabulary speech recognition"

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)