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: "on the choice of modeling unit for sequence to sequence speech recognition"
  Abstract: In conventional speech recognition, phoneme-based models outperform grapheme-based models for non-phonetic languages such as English. The performance gap between the two typically reduces as the amount of training data is increased. In this work, we examine the impact of the choice of modeling unit for attention-based encoder-decoder models. We conduct experiments on the LibriSpeech 100hr, 460hr, and 960hr tasks, using various target units (phoneme, grapheme, and word-piece); across all tasks, we find that grapheme or word-piece models consistently outperform phoneme-based models, even though they are evaluated without a lexicon or an external language model. We also investigate model complementarity: we find that we can improve WERs by up to 9% relative by rescoring N-best lists generated from a strong word-piece based baseline with either the phoneme or the grapheme model. Rescoring an N-best list generated by the phonemic system, however, provides limited improvements. Further analysis shows that the word-piece-based models produce more diverse N-best hypotheses, and thus lower oracle WERs, than phonemic models.

Connected through hyperedge HE-1 (induced by source paper "effectiveness of self supervised pre training for speech recognition"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "acoustically grounded word embeddings for improved acoustics to word speech recognition"
    - "cloze driven pretraining of self attention networks"
    - "transformer xl attentive language models beyond a fixed length context"
    - "an unsupervised autoregressive model for speech representation learning"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "self training for end to end speech recognition"

Connected through hyperedge HE-2 (induced by source paper "vq wav2vec self supervised learning of discrete speech representations"):
  with members:
    - "unsupervised acoustic unit discovery for speech synthesis using discrete latent variable neural networks"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "vqvae unsupervised unit discovery and multi scale code2spec inverter for zerospeech challenge 2019"
    - "pre trained language model representations for language generation"
    - "spanbert improving pre training by representing and predicting spans"
    - "unsupervised pre training of image features on non curated data"
    - "cloze driven pretraining of self attention networks"

Connected through hyperedge HE-3 (induced by source paper "language modeling with deep transformers"):
  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"
    - "generating wikipedia by summarizing long sequences"
    - "fully convolutional speech recognition"
    - "self attention with relative position representations"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "direct speech to speech translation with a sequence to sequence model"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "end to end spoken language translation"
    - "tied multitask learning for neural speech translation"
    - "neural voice cloning with a few samples"
    - "end to end automatic speech translation of audiobooks"
    - "efficient neural audio synthesis"
    - "conditional end to end audio transforms"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"

Connected through hyperedge HE-5 (induced by source paper "rwth asr systems for librispeech hybrid vs attention w o data augmentation"):
  with members:
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "language modeling with deep transformers"
    - "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-6 (induced by source paper "enforcing encoder decoder modularity in sequence to sequence models"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "a fully differentiable beam search decoder"
    - "massively multilingual adversarial speech recognition"
    - "espnet end to end speech processing toolkit"
    - "attention is all you need"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "joint ctc attention based end to end speech recognition using multi task learning"

Connected through hyperedge HE-7 (induced by source paper "phoneme based contextualization for cross lingual speech recognition in end to end models"):
  with members:
    - "streaming end to end speech recognition for mobile devices"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "no need for a lexicon evaluating the value of the pronunciation lexica in end to end models"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "tensorflow a system for large scale machine learning"
    - "sequence transduction with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "exploring lexicon free modeling units for end to end korean and korean english code switching speech recognition"):
  with members:
    - "a comparative study on transformer vs rnn in speech applications"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "espnet end to end speech processing toolkit"
    - "a comparable study of modeling units for end to end mandarin speech recognition"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "adadelta an adaptive learning rate method"

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)