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: "fast and accurate recurrent neural network acoustic models for speech recognition"
  Abstract: We have recently shown that deep Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as acoustic models for speech recognition. More recently, we have shown that the performance of sequence trained context dependent (CD) hidden Markov model (HMM) acoustic models using such LSTM RNNs can be equaled by sequence trained phone models initialized with connectionist temporal classification (CTC). In this paper, we present techniques that further improve performance of LSTM RNN acoustic models for large vocabulary speech recognition. We show that frame stacking and reduced frame rate lead to more accurate models and faster decoding. CD phone modeling leads to further improvements. We also present initial results for LSTM RNN models outputting words directly.

Connected through hyperedge HE-1 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks"):
  with members:
    - "an optimized recurrent unit for ultra low power keyword spotting"
    - "streaming end to end speech recognition for mobile devices"
    - "donut ctc based query by example keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "automatic grammar augmentation for robust voice command recognition"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "spoken language understanding on the edge"

Connected through hyperedge HE-2 (induced by source paper "hear no evil see kenansville efficient and transferable black box attacks on speech recognition and voice identification systems"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "practical hidden voice attacks against speech and speaker recognition systems"
    - "targeted adversarial examples for black box audio systems"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "adversarial patch"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "characterizing audio adversarial examples using temporal dependency"

Connected through hyperedge HE-3 (induced by source paper "deep audio visual speech recognition"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "large scale visual speech recognition"
    - "lrs3 ted a large scale dataset for visual speech recognition"
    - "end to end audiovisual speech recognition"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "wav2letter an end to end convnet based speech recognition system"
    - "lipnet end to end sentence level lipreading"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "synchronous speech recognition and speech to text translation with interactive decoding"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "attention passing models for robust and data efficient end to end speech translation"
    - "end to end speech translation with knowledge distillation"
    - "synchronous bidirectional inference for neural sequence generation"
    - "tied multitask learning for neural speech translation"
    - "stacl simultaneous translation with implicit anticipation and controllable latency using prefix to prefix framework"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"

Connected through hyperedge HE-6 (induced by source paper "end to end speech translation with knowledge distillation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "large scale distributed neural network training through online distillation"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "sequence to sequence models can directly translate foreign speech"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "a fully differentiable beam search decoder"):
  with members:
    - "fully convolutional speech recognition"
    - "minimum word error rate training for attention based sequence to sequence models"
    - "improving end to end speech recognition with policy learning"
    - "wav2letter an end to end convnet based speech recognition system"
    - "convolutional sequence to sequence learning"
    - "six challenges for neural machine translation"
    - "a continuous relaxation of beam search for end to end training of neural sequence models"
    - "cold fusion training seq2seq models together with language models"

Connected through hyperedge HE-8 (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"
    - "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"

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)