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: "no need for a lexicon evaluating the value of the pronunciation lexica in end to end models"
  Abstract: For decades, context-dependent phonemes have been the dominant sub-word unit for conventional acoustic modeling systems. This status quo has begun to be challenged recently by end-to-end models which seek to combine acoustic, pronunciation, and language model components into a single neural network. Such systems, which typically predict graphemes or words, simplify the recognition process since they remove the need for a separate expert-curated pronunciation lexicon to map from phoneme-based units to words. However, there has been little previous work comparing phoneme-based versus grapheme-based sub-word units in the end-to-end modeling framework, to determine whether the gains from such approaches are primarily due to the new probabilistic model, or from the joint learning of the various components with grapheme-based units. #R##N#In this work, we conduct detailed experiments which are aimed at quantifying the value of phoneme-based pronunciation lexica in the context of end-to-end models. We examine phoneme-based end-to-end models, which are contrasted against grapheme-based ones on a large vocabulary English Voice-search task, where we find that graphemes do indeed outperform phonemes. We also compare grapheme and phoneme-based approaches on a multi-dialect English task, which once again confirm the superiority of graphemes, greatly simplifying the system for recognizing multiple dialects.

Connected through hyperedge HE-1 (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"
    - "minimum word error rate training for attention based sequence to sequence models"

Connected through hyperedge HE-2 (induced by source paper "phoneme based contextualization for cross lingual speech recognition in end to end models"):
  with members:
    - "on the choice of modeling unit for sequence to sequence speech recognition"
    - "streaming end to end speech recognition for mobile devices"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "tensorflow a system for large scale machine learning"
    - "sequence transduction with recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "improving end to end speech recognition with pronunciation assisted sub word modeling"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "improved training of end to end attention models for speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-4 (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"
    - "minimum word error rate training for attention based sequence to sequence 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)