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: "language modeling with deep transformers"
  Abstract: We explore deep autoregressive Transformer models in language modeling for speech recognition. We focus on two aspects. First, we revisit Transformer model configurations specifically for language modeling. We show that well configured Transformer models outperform our baseline models based on the shallow stack of LSTM recurrent neural network layers. We carry out experiments on the open-source LibriSpeech 960hr task, for both 200K vocabulary word-level and 10K byte-pair encoding subword-level language modeling. We apply our word-level models to conventional hybrid speech recognition by lattice rescoring, and the subword-level models to attention based encoder-decoder models by shallow fusion. Second, we show that deep Transformer language models do not require positional encoding. The positional encoding is an essential augmentation for the self-attention mechanism which is invariant to sequence ordering. However, in autoregressive setup, as is the case for language modeling, the amount of information increases along the position dimension, which is a positional signal by its own. The analysis of attention weights shows that deep autoregressive self-attention models can automatically make use of such positional information. We find that removing the positional encoding even slightly improves the performance of these models.

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"
    - "a comparative study on transformer vs rnn in speech applications"
    - "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 "pseudolikelihood reranking with masked language models"):
  with members:
    - "passage re ranking with bert"
    - "effective sentence scoring method using bidirectional language model for speech recognition"
    - "pre trained language model representations for language generation"
    - "bertscore evaluating text generation with bert"
    - "transformer xl attentive language models beyond a fixed length context"
    - "language models with transformers"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-3 (induced by source paper "a comparative study on transformer vs rnn in speech applications"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "ted lium 3 twice as much data and corpus repartition for experiments on speaker adaptation"
    - "scaling neural machine translation"
    - "training tips for the transformer model"
    - "neural speech synthesis with transformer network"
    - "end to end speech recognition with word based rnn language models"

Connected through hyperedge HE-4 (induced by source paper "rwth asr systems for librispeech hybrid vs attention w o data augmentation"):
  with members:
    - "on the choice of modeling unit for sequence to sequence speech recognition"
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "the capio 2017 conversational speech recognition system"
    - "fully convolutional speech recognition"
    - "returnn as a generic flexible neural toolkit with application to translation and speech recognition"
    - "improved training of end to end attention models for speech recognition"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "optimal completion distillation for sequence learning"

Connected through hyperedge HE-5 (induced by source paper "an empirical study of efficient asr rescoring with transformers"):
  with members:
    - "scalable multi corpora neural language models for asr"
    - "spanbert improving pre training by representing and predicting spans"
    - "efficient dynamic wfst decoding for personalized language models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "knowledge distillation for recurrent neural network language modeling with trust regularization"
    - "roberta a robustly optimized bert pretraining approach"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character level language modeling with deeper self attention"

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"
    - "a comparative study on transformer vs rnn in speech applications"
    - "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 "generating synthetic audio data for attention based speech recognition systems"):
  with members:
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "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"
    - "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-8 (induced by source paper "long span language modeling for speech recognition"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "training tips for the transformer model"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "attention is all you need"
    - "notes on noise contrastive estimation and negative sampling"

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)