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: "deep recurrent neural networks for acoustic modelling"
  Abstract: We present a novel deep Recurrent Neural Network (RNN) model for acoustic modelling in Automatic Speech Recognition (ASR). We term our contribution as a TC-DNN-BLSTM-DNN model, the model combines a Deep Neural Network (DNN) with Time Convolution (TC), followed by a Bidirectional Long Short-Term Memory (BLSTM), and a final DNN. The first DNN acts as a feature processor to our model, the BLSTM then generates a context from the sequence acoustic signal, and the final DNN takes the context and models the posterior probabilities of the acoustic states. We achieve a 3.47 WER on the Wall Street Journal (WSJ) eval92 task or more than 8% relative improvement over the baseline DNN models.

Connected through hyperedge HE-1 (induced by source paper "who needs words lexicon free speech recognition"):
  with members:
    - "the capio 2017 conversational speech recognition system"
    - "fully convolutional speech recognition"
    - "wav2letter the fastest open source speech recognition system"
    - "improved training of end to end attention models for speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "comparison of decoding strategies for ctc acoustic models"
    - "wav2letter an end to end convnet based speech recognition system"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "ims speech a speech to text tool"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "the capio 2017 conversational speech recognition system"
    - "ted lium 3 twice as much data and corpus repartition for experiments on speaker adaptation"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "open source automatic speech recognition for german"
    - "improved training of end to end attention models for speech recognition"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "to reverse the gradient or not an empirical comparison of adversarial and multi task learning in speech recognition"):
  with members:
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "domain adversarial training for accented speech recognition"
    - "english conversational telephone speech recognition by humans and machines"
    - "letter based speech recognition with gated convnets"
    - "an adversarial regularisation for semi supervised training of structured output neural networks"
    - "wav2letter an end to end convnet based speech recognition system"
    - "an overview of multi task learning in deep neural networks"
    - "fader networks manipulating images by sliding attributes"

Connected through hyperedge HE-4 (induced by source paper "fully convolutional speech recognition"):
  with members:
    - "the capio 2017 conversational speech recognition system"
    - "end to end speech recognition from the raw waveform"
    - "improved training of end to end attention models for speech recognition"
    - "wav2letter an end to end convnet based speech recognition system"
    - "convolutional sequence to sequence learning"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "instance normalization the missing ingredient for fast stylization"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "on training recurrent networks with truncated backpropagation through time in speech recognition"):
  with members:
    - "a study of enhancement augmentation and autoencoder methods for domain adaptation in distant speech recognition"
    - "an analysis of neural language modeling at multiple scales"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "regularizing and optimizing lstm language models"
    - "architectural complexity measures of recurrent neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "speech recognition with deep recurrent neural networks"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "letter based speech recognition with gated convnets"):
  with members:
    - "improved training of end to end attention models for speech recognition"
    - "english conversational telephone speech recognition by humans and machines"
    - "end to end neural segmental models for speech recognition"
    - "improving end to end speech recognition with policy learning"
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"
    - "wav2letter an end to end convnet based speech recognition system"
    - "convolutional sequence to sequence learning"
    - "towards better decoding and language model integration in sequence to sequence models"

Connected through hyperedge HE-7 (induced by source paper "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"):
  with members:
    - "lstm a search space odyssey"
    - "associative long short term memory"
    - "returnn the rwth extensible training framework for universal recurrent neural networks"
    - "deep learning in neural networks"
    - "benchmarking of lstm networks"
    - "deep residual learning for image recognition"
    - "highway long short term memory rnns for distant speech recognition"
    - "training very deep networks"

Connected through hyperedge HE-8 (induced by source paper "simplified end to end mmi training and voting for asr"):
  with members:
    - "english conversational telephone speech recognition by humans and machines"
    - "wav2letter an end to end convnet based speech recognition system"
    - "residual convolutional ctc networks for automatic speech recognition"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "personalized speech recognition on mobile devices"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "achieving human parity in conversational speech recognition"
    - "listen attend and spell"

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.LG(Machine Learning)