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: "back translation style data augmentation for end to end asr"
  Abstract: In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paired with speech signals. Inspired by the back-translation technique proposed in the field of machine translation, we build a neural text-to-encoder model which predicts a sequence of hidden states extracted by a pre-trained E2E-ASR encoder from a sequence of characters. By using hidden states as a target instead of acoustic features, it is possible to achieve faster attention learning and reduce computational cost, thanks to sub-sampling in E2E-ASR encoder, also the use of the hidden states can avoid to model speaker dependencies unlike acoustic features. After training, the text-to-encoder model generates the hidden states from a large amount of unpaired text, then E2E-ASR decoder is retrained using the generated hidden states as additional training data. Experimental evaluation using LibriSpeech dataset demonstrates that our proposed method achieves improvement of ASR performance and reduces the number of unknown words without the need for paired data.

Connected through hyperedge HE-1 (induced by source paper "self supervised speech recognition via local prior matching"):
  with members:
    - "attentive adversarial learning for domain invariant training"
    - "parallel neural text to speech"
    - "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"
    - "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-2 (induced by source paper "self training for end to end speech recognition"):
  with members:
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "lessons from building acoustic models with a million hours of speech"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "cycle consistency training for end to end speech recognition"
    - "adversarial training of end to end speech recognition using a criticizing language model"
    - "fully convolutional speech recognition"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-3 (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"
    - "waveglow a flow based generative network for speech synthesis"
    - "espnet end to end speech processing toolkit"
    - "close to human quality tts with transformer"
    - "forward attention in sequence to sequence acoustic modeling for speech synthesis"

Connected through hyperedge HE-4 (induced by source paper "leveraging out of task data for end to end automatic speech translation"):
  with members:
    - "transformers with convolutional context for asr"
    - "attention passing models for robust and data efficient end to end speech translation"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "espnet end to end speech processing toolkit"
    - "end to end automatic speech translation of audiobooks"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "harnessing indirect training data for end to end automatic speech translation tricks of the trade"):
  with members:
    - "transformers with convolutional context for asr"
    - "attention passing models for robust and data efficient end to end speech translation"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "espnet end to end speech processing toolkit"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "leveraging weakly supervised data to improve end to end speech to text translation"
    - "sequence to sequence models can directly translate foreign speech"

Connected through hyperedge HE-6 (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"
    - "machine speech chain with one shot speaker adaptation"
    - "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-7 (induced by source paper "sequence to sequence automatic speech recognition with word embedding regularization and fused decoding"):
  with members:
    - "rewe regressing word embeddings for regularization of neural machine translation systems"
    - "adversarial training of end to end speech recognition using a criticizing language model"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "state of the art speech recognition with sequence to sequence models"
    - "listening while speaking speech chain by deep learning"
    - "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"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-8 (induced by source paper "leveraging weakly supervised data to improve end to end speech to text translation"):
  with members:
    - "tied multitask learning for neural speech translation"
    - "machine speech chain with one shot speaker adaptation"
    - "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"

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)