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: "advancing acoustic to word ctc model"
  Abstract: The acoustic-to-word model based on the connectionist temporal classification (CTC) criterion was shown as a natural end-to-end (E2E) model directly targeting words as output units. However, the word-based CTC model suffers from the out-of-vocabulary (OOV) issue as it can only model limited number of words in the output layer and maps all the remaining words into an OOV output node. Hence, such a word-based CTC model can only recognize the frequent words modeled by the network output nodes. Our first attempt to improve the acoustic-to-word model is a hybrid CTC model which consults a letter-based CTC when the word-based CTC model emits OOV tokens during testing time. Then, we propose a much better solution by training a mixed-unit CTC model which decomposes all the OOV words into sequences of frequent words and multi-letter units. Evaluated on a 3400 hours Microsoft Cortana voice assistant task, the final acoustic-to-word solution improves the baseline word-based CTC by relative 12.09% word error rate (WER) reduction when combined with our proposed attention CTC. Such an E2E model without using any language model (LM) or complex decoder outperforms the traditional context-dependent phoneme CTC which has strong LM and decoder by relative 6.79%.

Connected through hyperedge HE-1 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language"):
  with members:
    - "how does learning rate decay help modern neural 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"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "tied multitask learning for neural speech translation"
    - "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "state of the art speech recognition with sequence to sequence models"

Connected through hyperedge HE-2 (induced by source paper "accelerating rnn transducer inference via one step constrained beam search"):
  with members:
    - "rnn t for latency controlled asr with improved beam search"
    - "vectorization of hypotheses and speech for faster beam search in encoder decoder based speech recognition"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "streaming end to end speech recognition for mobile devices"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "state of the art speech recognition with sequence to sequence models"

Connected through hyperedge HE-3 (induced by source paper "speaker aware speech transformer"):
  with members:
    - "speaker adaptation for end to end ctc models"
    - "aishell 2 transforming mandarin asr research into industrial scale"
    - "attention is all you need"
    - "embedding based speaker adaptive training of deep neural networks"
    - "aishell 1 an open source mandarin speech corpus and a speech recognition baseline"
    - "end to end attention based large vocabulary speech recognition"
    - "attention based models for speech recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-4 (induced by source paper "acoustic to word models with conversational context information"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "hierarchical multi task learning with ctc"
    - "dialog context aware end to end speech recognition"
    - "multi modal data augmentation for end to end asr"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "improved training of end to end attention models for speech recognition"
    - "english conversational telephone speech recognition by humans and machines"
    - "building competitive direct acoustics to word models for english conversational speech recognition"

Connected through hyperedge HE-5 (induced by source paper "gated embeddings in end to end speech recognition for conversational context fusion"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improved training of end to end attention models for speech recognition"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "towards language universal end to end speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "fasttext zip compressing text classification models"

Connected through hyperedge HE-6 (induced by source paper "improving oov detection and resolution with external language models in acoustic to word asr"):
  with members:
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "english conversational telephone speech recognition by humans and machines"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "acoustic to word model without oov"
    - "comparing human and machine errors in conversational speech transcription"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "tying word vectors and word classifiers a loss framework for language modeling"

Connected through hyperedge HE-7 (induced by source paper "learned in speech recognition contextual acoustic word embeddings"):
  with members:
    - "deep contextualized word representations"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "learning acoustic word embeddings with temporal context for query by example speech search"
    - "how2 a large scale dataset for multimodal language understanding"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "improved training of end to end attention models for speech recognition"
    - "senteval an evaluation toolkit for universal sentence representations"

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"
    - "wav2letter the fastest open source speech recognition system"
    - "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)