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 modular training of neural acoustics to word model for lvcsr"
  Abstract: End-to-end (E2E) automatic speech recognition (ASR) systems directly map acoustics to words using a unified model. Previous works mostly focus on E2E training a single model which integrates acoustic and language model into a whole. Although E2E training benefits from sequence modeling and simplified decoding pipelines, large amount of transcribed acoustic data is usually required, and traditional acoustic and language modelling techniques cannot be utilized. In this paper, a novel modular training framework of E2E ASR is proposed to separately train neural acoustic and language models during training stage, while still performing end-to-end inference in decoding stage. Here, an acoustics-to-phoneme model (A2P) and a phoneme-to-word model (P2W) are trained using acoustic data and text data respectively. A phone synchronous decoding (PSD) module is inserted between A2P and P2W to reduce sequence lengths without precision loss. Finally, modules are integrated into an acousticsto-word model (A2W) and jointly optimized using acoustic data to retain the advantage of sequence modeling. Experiments on a 300- hour Switchboard task show significant improvement over the direct A2W model. The efficiency in both training and decoding also benefits from the proposed method.

Connected through hyperedge HE-1 (induced by source paper "learned in speech recognition contextual acoustic word embeddings"):
  with members:
    - "advancing acoustic to word ctc model"
    - "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"
    - "improved training of end to end attention models for speech recognition"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-2 (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"
    - "direct acoustics to word models for english conversational speech recognition"
    - "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-3 (induced by source paper "audio caption listen and tell"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "end to end contextual speech recognition using class language models and a token passing decoder"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "automated audio captioning with recurrent neural networks"
    - "weakly supervised dense video captioning"
    - "multi task video captioning with video and entailment generation"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-4 (induced by source paper "end to end anchored speech recognition"):
  with members:
    - "mixed precision training for nlp and speech recognition with openseq2seq"
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "end to end contextual speech recognition using class language models and a token passing decoder"
    - "deep extractor network for target speaker recovery from single channel speech mixtures"
    - "state of the art speech recognition with sequence to sequence models"
    - "end to end text dependent speaker verification"
    - "attention based models for speech recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (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"
    - "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"
    - "direct acoustics to word models for english conversational speech recognition"

Connected through hyperedge HE-6 (induced by source paper "improving end to end speech recognition with pronunciation assisted sub word modeling"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "improved training of end to end attention models for speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "no need for a lexicon evaluating the value of the pronunciation lexica in end to end models"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-7 (induced by source paper "sequence discriminative training for deep learning based acoustic keyword spotting"):
  with members:
    - "a gpu based wfst decoder with exact lattice generation"
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "developing far field speaker system via teacher student learning"
    - "end to end asr free keyword search from speech"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "speech recognition with deep recurrent neural networks"
    - "sequence transduction with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "linguistic search optimization for deep learning based lvcsr"):
  with members:
    - "a gpu based wfst decoder with exact lattice generation"
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "wav2letter an end to end convnet based speech recognition system"
    - "personalized speech recognition on mobile devices"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "fast and accurate recurrent neural network acoustic models for 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)