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: "acoustic to word model without oov"
  Abstract: Recently, the acoustic-to-word model based on the Connectionist Temporal Classification (CTC) criterion was shown as a natural end-to-end model directly targeting words as output units. However, this type of 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. Therefore, such word-based CTC model can only recognize the frequent words modeled by the network output nodes. It also cannot easily handle the hot-words which emerge after the model is trained. In this study, we improve the acoustic-to-word model with a hybrid CTC model which can predict both words and characters at the same time. With a shared-hidden-layer structure and modular design, the alignments of words generated from the word-based CTC and the character-based CTC are synchronized. Whenever the acoustic-to-word model emits an OOV token, we back off that OOV segment to the word output generated from the character-based CTC, hence solving the OOV or hot-words issue. Evaluated on a Microsoft Cortana voice assistant task, the proposed model can reduce the errors introduced by the OOV output token in the acoustic-to-word model by 30%.

Connected through hyperedge HE-1 (induced by source paper "improving oov detection and resolution with external language models in acoustic to word asr"):
  with members:
    - "advancing acoustic to word ctc model"
    - "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"
    - "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-2 (induced by source paper "advancing acoustic to word ctc model with attention and mixed units"):
  with members:
    - "advancing acoustic to word ctc model"
    - "developing far field speaker system via teacher student learning"
    - "advancing connectionist temporal classification with attention modeling"
    - "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"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"

Connected through hyperedge HE-3 (induced by source paper "pushing the boundaries of audiovisual word recognition using residual networks and lstms"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "the conversation deep audio visual speech enhancement"
    - "advancing acoustic to word ctc model"
    - "end to end audiovisual speech recognition"
    - "zero shot keyword spotting for visual speech recognition in the wild"
    - "improving speaker independent lipreading with domain adversarial training"
    - "attention is all you need"
    - "deep word embeddings for visual speech recognition"

Connected through hyperedge HE-4 (induced by source paper "on the inductive bias of word character level multi task learning for speech recognition"):
  with members:
    - "advancing acoustic to word ctc model"
    - "hierarchical multi task learning with ctc"
    - "hierarchical multitask learning for ctc based speech recognition"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "optimizing expected word error rate via sampling for speech recognition"
    - "improving end to end speech recognition with policy learning"
    - "direct acoustics to word models for english conversational speech recognition"
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"

Connected through hyperedge HE-5 (induced by source paper "end to end speech recognition with word based rnn language models"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "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"
    - "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"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "very deep convolutional networks for end to end speech recognition"
    - "joint ctc attention based end to end speech recognition using multi task learning"

Connected through hyperedge HE-6 (induced by source paper "subword and crossword units for ctc acoustic models"):
  with members:
    - "advancing acoustic to word ctc model"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "direct acoustics to word models for english conversational speech recognition"
    - "stronger baselines for trustable results in neural machine translation"
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"
    - "comparison of decoding strategies for ctc acoustic models"
    - "advances in all neural 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)