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: "a gpu based wfst decoder with exact lattice generation"
  Abstract: We describe initial work on an extension of the Kaldi toolkit that supports weighted finite-state transducer (WFST) decoding on Graphics Processing Units (GPUs). We implement token recombination as an atomic GPU operation in order to fully parallelize the Viterbi beam search, and propose a dynamic load balancing strategy for more efficient token passing scheduling among GPU threads. We also redesign the exact lattice generation and lattice pruning algorithms for better utilization of the GPUs. Experiments on the Switchboard corpus show that the proposed method achieves identical 1-best results and lattice quality in recognition and confidence measure tasks, while running 3 to 15 times faster than the single process Kaldi decoder. The above results are reported on different GPU architectures. Additionally we obtain a 46-fold speedup with sequence parallelism and multi-process service (MPS) in GPU.

Connected through hyperedge HE-1 (induced by source paper "neural zero inflated quality estimation model for automatic speech recognition system"):
  with members:
    - "lattice transformer for speech translation"
    - "cross lingual language model pretraining"
    - "bilingual expert can find translation errors"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "gpu accelerated viterbi exact lattice decoder for batched online and offline speech recognition"):
  with members:
    - "streaming end to end speech recognition for mobile devices"
    - "decoding with finite state transducers on gpus"
    - "personalized speech recognition on mobile devices"

Connected through hyperedge HE-3 (induced by source paper "sequence discriminative training for deep learning based acoustic keyword spotting"):
  with members:
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "developing far field speaker system via teacher student learning"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "end to end asr free keyword search from speech"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "speech recognition with deep recurrent neural networks"
    - "sequence transduction with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "vectorization of hypotheses and speech for faster beam search in encoder decoder based speech recognition"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "advancing acoustic to word ctc model"
    - "improved training of end to end attention models for speech recognition"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "the microsoft 2016 conversational speech recognition system"

Connected through hyperedge HE-5 (induced by source paper "linguistic search optimization for deep learning based lvcsr"):
  with members:
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "wav2letter an end to end convnet based speech recognition system"
    - "personalized speech recognition on mobile devices"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"

Connected through hyperedge HE-6 (induced by source paper "end to end contextual speech recognition using class language models and a token passing decoder"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "linguistic search optimization for deep learning based lvcsr"
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "personalized speech recognition on mobile devices"
    - "neural speech recognizer acoustic to word lstm model for 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)