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: "direct acoustics to word models for english conversational speech recognition"
  Abstract: Recent work on end-to-end automatic speech recognition (ASR) has shown that the connectionist temporal classification (CTC) loss can be used to convert acoustics to phone or character sequences. Such systems are used with a dictionary and separately-trained Language Model (LM) to produce word sequences. However, they are not truly end-to-end in the sense of mapping acoustics directly to words without an intermediate phone representation. In this paper, we present the first results employing direct acoustics-to-word CTC models on two well-known public benchmark tasks: Switchboard and CallHome. These models do not require an LM or even a decoder at run-time and hence recognize speech with minimal complexity. However, due to the large number of word output units, CTC word models require orders of magnitude more data to train reliably compared to traditional systems. We present some techniques to mitigate this issue. Our CTC word model achieves a word error rate of 13.0%/18.8% on the Hub5-2000 Switchboard/CallHome test sets without any LM or decoder compared with 9.6%/16.0% for phone-based CTC with a 4-gram LM. We also present rescoring results on CTC word model lattices to quantify the performance benefits of a LM, and contrast the performance of word and phone CTC models.

Connected through hyperedge HE-1 (induced by source paper "acoustic to word models with conversational context information"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "advancing acoustic to word ctc model"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "gated embeddings in end to end speech recognition for conversational context fusion"):
  with members:
    - "advancing acoustic to word ctc model"
    - "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"
    - "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-3 (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"
    - "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-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "speaker adaptation for end to end ctc models"):
  with members:
    - "advancing acoustic to word ctc model"
    - "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"
    - "embedding based speaker adaptive training of deep neural networks"
    - "exploring neural transducers for end to end speech recognition"
    - "improving the performance of online neural transducer models"
    - "learning hidden unit contributions for unsupervised acoustic model adaptation"

Connected through hyperedge HE-6 (induced by source paper "guiding ctc posterior spike timings for improved posterior fusion and knowledge distillation"):
  with members:
    - "improved training for online end to end speech recognition systems"
    - "english conversational telephone speech recognition by humans and machines"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"
    - "residual convolutional ctc networks for automatic speech recognition"
    - "simplified end to end mmi training and voting for asr"
    - "advances in all neural speech recognition"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"

Connected through hyperedge HE-7 (induced by source paper "minimum bayes risk training of rnn transducer for end to end speech recognition"):
  with members:
    - "improving rnn transducer modeling for end to end speech recognition"
    - "transformer based acoustic modeling for hybrid speech recognition"
    - "streaming end to end speech recognition for mobile devices"
    - "exploring rnn transducer for chinese speech recognition"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "optimizing expected word error rate via sampling for speech recognition"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "acoustically grounded word embeddings for improved acoustics to word speech recognition"):
  with members:
    - "advancing acoustic to word ctc model"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "hierarchical multitask learning for ctc based speech recognition"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "end to end asr free keyword search from speech"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"

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)