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: "wav2letter the fastest open source speech recognition system"
  Abstract: This paper introduces wav2letter++, the fastest open-source deep learning speech recognition framework. wav2letter++ is written entirely in C++, and uses the ArrayFire tensor library for maximum efficiency. Here we explain the architecture and design of the wav2letter++ system and compare it to other major open-source speech recognition systems. In some cases wav2letter++ is more than 2x faster than other optimized frameworks for training end-to-end neural networks for speech recognition. We also show that wav2letter++'s training times scale linearly to 64 GPUs, the highest we tested, for models with 100 million parameters. High-performance frameworks enable fast iteration, which is often a crucial factor in successful research and model tuning on new datasets and tasks.

Connected through hyperedge HE-1 (induced by source paper "sequence to sequence speech recognition with time depth separable convolutions"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "the capio 2017 conversational speech recognition system"
    - "fully convolutional speech recognition"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "exploring the limits of weakly supervised pretraining"
    - "improved training of end to end attention models for speech recognition"
    - "understanding back translation at scale"

Connected through hyperedge HE-2 (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"
    - "back translation style data augmentation for end to end asr"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "cycle consistency training for end to end speech recognition"

Connected through hyperedge HE-3 (induced by source paper "who needs words lexicon free speech recognition"):
  with members:
    - "the capio 2017 conversational speech recognition system"
    - "fully convolutional speech recognition"
    - "improved training of end to end attention models for speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "comparison of decoding strategies for ctc acoustic models"
    - "wav2letter an end to end convnet based speech recognition system"
    - "convolutional sequence to sequence learning"
    - "towards end to end speech recognition with deep convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "cif continuous integrate and fire for end to end speech recognition"):
  with members:
    - "transformers with convolutional context for asr"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "self attention aligner a latency control end to end model for asr using self attention network and chunk hopping"
    - "espnet end to end speech processing toolkit"
    - "an online attention based model for speech recognition"
    - "aishell 2 transforming mandarin asr research into industrial scale"
    - "improved training of end to end attention models for speech recognition"
    - "extending recurrent neural aligner for streaming end to end speech recognition in mandarin"

Connected through hyperedge HE-5 (induced by source paper "end to end asr from supervised to semi supervised learning with modern architectures"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "reducing transformer depth on demand with structured dropout"
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "word level speech recognition with a dynamic lexicon"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "self training for end to end speech recognition"
    - "state of the art speech recognition using multi stream self attention with dilated 1d convolutions"

Connected through hyperedge HE-6 (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"
    - "back translation style data augmentation for end to end asr"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "wav2vec unsupervised pre training for speech recognition"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pre trained language model representations for language generation"
    - "cloze driven pretraining of self attention networks"
    - "who needs words lexicon free speech recognition"
    - "data efficient image recognition with contrastive predictive coding"
    - "cross lingual language model pretraining"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "improving speech emotion recognition via transformer based predictive coding through transfer learning"

Connected through hyperedge HE-8 (induced by source paper "word level speech recognition with a dynamic lexicon"):
  with members:
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "who needs words lexicon free speech recognition"
    - "a fully differentiable beam search decoder"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "advancing acoustic to word ctc model"
    - "improved training of end to end attention models for speech recognition"
    - "minimum word error rate training for attention based sequence to sequence models"
    - "direct acoustics to word models for english conversational 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)