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: "a comparative study on transformer vs rnn in speech applications"
  Abstract: Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS). This paper focuses on an emergent sequence-to-sequence model called Transformer, which achieves state-of-the-art performance in neural machine translation and other natural language processing applications. We undertook intensive studies in which we experimentally compared and analyzed Transformer and conventional recurrent neural networks (RNN) in a total of 15 ASR, one multilingual ASR, one ST, and two TTS benchmarks. Our experiments revealed various training tips and significant performance benefits obtained with Transformer for each task including the surprising superiority of Transformer in 13/15 ASR benchmarks in comparison with RNN. We are preparing to release Kaldi-style reproducible recipes using open source and publicly available datasets for all the ASR, ST, and TTS tasks for the community to succeed our exciting outcomes.

Connected through hyperedge HE-1 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition"):
  with members:
    - "q8bert quantized 8bit bert"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "language modeling with deep transformers"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "fully quantized transformer for improved translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "streaming end to end speech recognition for mobile devices"

Connected through hyperedge HE-2 (induced by source paper "scaling up online speech recognition using convnets"):
  with members:
    - "reducing transformer depth on demand with structured dropout"
    - "rnn t for latency controlled asr with improved beam search"
    - "end to end asr from supervised to semi supervised learning with modern architectures"
    - "transformer based acoustic modeling for hybrid speech recognition"
    - "streaming end to end speech recognition for mobile devices"
    - "fully convolutional speech recognition"
    - "efficient neural audio synthesis"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-3 (induced by source paper "training asr models by generation of contextual information"):
  with members:
    - "transformers with convolutional context for asr"
    - "lessons from building acoustic models with a million hours of speech"
    - "wav2vec unsupervised pre training for speech recognition"
    - "roberta a robustly optimized bert pretraining approach"
    - "hierarchical neural story generation"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"

Connected through hyperedge HE-4 (induced by source paper "transformer based acoustic modeling for hybrid speech recognition"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "deja vu double feature presentation in deep transformer networks"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "state of the art speech recognition using multi stream self attention with dilated 1d convolutions"
    - "streaming end to end speech recognition for mobile devices"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self attentional acoustic models"

Connected through hyperedge HE-5 (induced by source paper "improving transformer based speech recognition using unsupervised pre training"):
  with members:
    - "pre trained language model representations for language generation"
    - "an unsupervised autoregressive model for speech representation learning"
    - "learning problem agnostic speech representations from multiple self supervised tasks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "wav2vec unsupervised pre training for speech recognition"
    - "self attention aligner a latency control end to end model for asr using self attention network and chunk hopping"
    - "roberta a robustly optimized bert pretraining approach"
    - "improving speech emotion recognition via transformer based predictive coding through transfer learning"

Connected through hyperedge HE-6 (induced by source paper "fully quantizing a simplified transformer for end to end speech recognition"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "language modeling with deep transformers"
    - "fully quantized transformer for improved translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "streaming end to end speech recognition for mobile devices"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-7 (induced by source paper "semantic mask for transformer based end to end speech recognition"):
  with members:
    - "transformers with convolutional context for asr"
    - "end to end asr from supervised to semi supervised learning with modern architectures"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "state of the art speech recognition using multi stream self attention with dilated 1d convolutions"
    - "transformer based acoustic modeling for hybrid speech recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "state of the art speech recognition using multi stream self attention with dilated 1d convolutions"):
  with members:
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "the capio 2017 conversational speech recognition system"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a novel pyramidal fsmn architecture with lattice free mmi for speech recognition"
    - "attention is all you need"

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)