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: "optimizing expected word error rate via sampling for speech recognition"
  Abstract: State-level minimum Bayes risk (sMBR) training has become the de facto standard for sequence-level training of speech recognition acoustic models. It has an elegant formulation using the expectation semiring, and gives large improvements in word error rate (WER) over models trained solely using cross-entropy (CE) or connectionist temporal classification (CTC). sMBR training optimizes the expected number of frames at which the reference and hypothesized acoustic states differ. It may be preferable to optimize the expected WER, but WER does not interact well with the expectation semiring, and previous approaches based on computing expected WER exactly involve expanding the lattices used during training. In this paper we show how to perform optimization of the expected WER by sampling paths from the lattices used during conventional sMBR training. The gradient of the expected WER is itself an expectation, and so may be approximated using Monte Carlo sampling. We show experimentally that optimizing WER during acoustic model training gives 5% relative improvement in WER over a well-tuned sMBR baseline on a 2-channel query recognition task (Google Home).

Connected through hyperedge HE-1 (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"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"

Connected through hyperedge HE-2 (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"
    - "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"
    - "wav2letter an end to end convnet based speech recognition system"

Connected through hyperedge HE-3 (induced by source paper "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"):
  with members:
    - "recurrent highway networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "listen attend and spell"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "end to end attention based large vocabulary speech recognition"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "a comparison of lattice free discriminative training criteria for purely sequence trained neural network acoustic models"):
  with members:
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "sequence discriminative training for deep learning based acoustic keyword spotting"
    - "the ibm 2016 english conversational telephone speech recognition system"
    - "achieving human parity in conversational speech recognition"

Connected through hyperedge HE-5 (induced by source paper "combining natural gradient with hessian free methods for sequence training"):
  with members:
    - "sequence training of dnn acoustic models with natural gradient"
    - "parallel training of dnns with natural gradient and parameter averaging"
    - "revisiting natural gradient for deep networks"

Connected through hyperedge HE-6 (induced by source paper "discriminative training of rnnlms with the average word error criterion"):
  with members:
    - "large margin neural language model"
    - "contrastive entropy a new evaluation metric for unnormalized language models"
    - "scaling recurrent neural network language models"

Connected through hyperedge HE-7 (induced by source paper "sequence prediction with neural segmental models"):
  with members:
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"
    - "wav2letter an end to end convnet based speech recognition system"
    - "an embedded segmental k means model for unsupervised segmentation and clustering of speech"
    - "multitask learning with ctc and segmental crf for speech recognition"
    - "end to end training approaches for discriminative segmental models"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "advances in all neural speech recognition"

Connected through hyperedge HE-8 (induced by source paper "streaming small footprint keyword spotting using sequence to sequence models"):
  with members:
    - "lstm a search space odyssey"
    - "end to end asr free keyword search from speech"
    - "max pooling loss training of long short term memory networks for small footprint keyword spotting"
    - "personalized speech recognition on mobile devices"
    - "on the efficient representation and execution of deep acoustic models"
    - "an end to end architecture for keyword spotting and voice activity detection"
    - "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)