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: "minimum word error rate training for attention based sequence to sequence models"
  Abstract: Sequence-to-sequence models, such as attention-based models in automatic speech recognition (ASR), are typically trained to optimize the cross-entropy criterion which corresponds to improving the log-likelihood of the data. However, system performance is usually measured in terms of word error rate (WER), not log-likelihood. Traditional ASR systems benefit from discriminative sequence training which optimizes criteria such as the state-level minimum Bayes risk (sMBR) which are more closely related to WER. In the present work, we explore techniques to train attention-based models to directly minimize expected word error rate. We consider two loss functions which approximate the expected number of word errors: either by sampling from the model, or by using N-best lists of decoded hypotheses, which we find to be more effective than the sampling-based method. In experimental evaluations, we find that the proposed training procedure improves performance by up to 8.2% relative to the baseline system. This allows us to train grapheme-based, uni-directional attention-based models which match the performance of a traditional, state-of-the-art, discriminative sequence-trained system on a mobile voice-search task.

Connected through hyperedge HE-1 (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"
    - "direct acoustics to word models for english conversational speech recognition"

Connected through hyperedge HE-2 (induced by source paper "a fully differentiable beam search decoder"):
  with members:
    - "fully convolutional speech recognition"
    - "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"
    - "sequence to sequence learning as beam search optimization"

Connected through hyperedge HE-3 (induced by source paper "on the choice of modeling unit for sequence to sequence speech recognition"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "the capio 2017 conversational speech recognition system"
    - "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"
    - "optimal completion distillation for sequence learning"
    - "sequence to sequence models can directly translate foreign speech"
    - "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"

Connected through hyperedge HE-4 (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-5 (induced by source paper "two pass end to end speech recognition"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "streaming end to end speech recognition for mobile devices"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "personalized speech recognition on mobile devices"
    - "joint ctc attention based end to end speech recognition using multi task learning"

Connected through hyperedge HE-6 (induced by source paper "returnn as a generic flexible neural toolkit with application to translation and speech recognition"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "machine translation at booking com journey and lessons learned"
    - "english conversational telephone speech recognition by humans and machines"
    - "sockeye a toolkit for neural machine translation"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "classical structured prediction losses for sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "chargrid towards understanding 2d documents"):
  with members:
    - "document image binarization with fully convolutional neural networks"
    - "focal loss for dense object detection"
    - "cloudscan a configuration free invoice analysis system using recurrent neural networks"
    - "learning to extract semantic structure from documents using multimodal fully convolutional neural network"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "neural architectures for named entity recognition"
    - "multilingual language processing from bytes"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-8 (induced by source paper "state of the art speech recognition with sequence to sequence models"):
  with members:
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "online and linear time attention by enforcing monotonic alignments"
    - "no need for a lexicon evaluating the value of the pronunciation lexica in end to end models"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "an online sequence to sequence model using partial conditioning"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "end to end attention based large vocabulary 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)