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: "improving the performance of online neural transducer models"
  Abstract: Having a sequence-to-sequence model which can operate in an online fashion is important for streaming applications such as Voice Search. Neural transducer is a streaming sequence-to-sequence model, but has shown a significant degradation in performance compared to non-streaming models such as Listen, Attend and Spell (LAS). In this paper, we present various improvements to NT. Specifically, we look at increasing the window over which NT computes attention, mainly by looking backwards in time so the model still remains online. In addition, we explore initializing a NT model from a LAS-trained model so that it is guided with a better alignment. Finally, we explore including stronger language models such as using wordpiece models, and applying an external LM during the beam search. On a Voice Search task, we find with these improvements we can get NT to match the performance of LAS.

Connected through hyperedge HE-1 (induced by source paper "streaming automatic speech recognition with the transformer model"):
  with members:
    - "improving rnn transducer modeling for end to end speech recognition"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "monotonic chunkwise attention"
    - "attention is all you need"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "first pass large vocabulary continuous speech recognition using bi directional recurrent dnns"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence transduction with recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "improving rnn transducer modeling for end to end speech recognition"):
  with members:
    - "advancing acoustic to word ctc model with attention and mixed units"
    - "advancing acoustic to word ctc model"
    - "layer trajectory lstm"
    - "advancing connectionist temporal classification with attention modeling"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "monotonic chunkwise attention"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "exploring neural transducers for end to end speech recognition"

Connected through hyperedge HE-3 (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"
    - "learning hidden unit contributions for unsupervised acoustic model adaptation"

Connected through hyperedge HE-4 (induced by source paper "end to end speech recognition using a high rank lstm ctc based model"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "advancing connectionist temporal classification with attention modeling"
    - "improved training of end to end attention models for speech recognition"
    - "improved training for online end to end speech recognition systems"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "state of the art speech recognition with sequence to sequence models"
    - "improving end to end speech recognition with policy learning"
    - "exploring neural transducers for end to end speech recognition"

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"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "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-6 (induced by source paper "an online attention based model for speech recognition"):
  with members:
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "extending recurrent neural aligner for streaming end to end speech recognition in mandarin"
    - "online and linear time attention by enforcing monotonic alignments"
    - "monotonic chunkwise attention"
    - "learning hard alignments with variational inference"
    - "exploring neural transducers for end to end speech recognition"
    - "local monotonic attention mechanism for end to end speech and language processing"
    - "online segment to segment neural transduction"

Connected through hyperedge HE-7 (induced by source paper "advancing connectionist temporal classification with attention modeling"):
  with members:
    - "advancing acoustic to word ctc model"
    - "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"
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "exploring neural transducers for end to end speech recognition"
    - "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)