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: "comparison of decoding strategies for ctc acoustic models"
  Abstract: Connectionist Temporal Classification has recently attracted a lot of interest as it offers an elegant approach to building acoustic models (AMs) for speech recognition. The CTC loss function maps an input sequence of observable feature vectors to an output sequence of symbols. Output symbols are conditionally independent of each other under CTC loss, so a language model (LM) can be incorporated conveniently during decoding, retaining the traditional separation of acoustic and linguistic components in ASR. For fixed vocabularies, Weighted Finite State Transducers provide a strong baseline for efficient integration of CTC AMs with n-gram LMs. Character-based neural LMs provide a straight forward solution for open vocabulary speech recognition and all-neural models, and can be decoded with beam search. Finally, sequence-to-sequence models can be used to translate a sequence of individual sounds into a word string. We compare the performance of these three approaches, and analyze their error patterns, which provides insightful guidance for future research and development in this important area.

Connected through hyperedge HE-1 (induced by source paper "who needs words lexicon free speech recognition"):
  with members:
    - "the capio 2017 conversational speech recognition system"
    - "fully convolutional speech recognition"
    - "wav2letter the fastest open source speech recognition system"
    - "improved training of end to end attention models for speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "wav2letter an end to end convnet based speech recognition system"
    - "convolutional sequence to sequence learning"
    - "towards end to end speech recognition with deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "phoneme level language models for sequence based low resource asr"):
  with members:
    - "domain robust feature extraction for rapid low resource asr development"
    - "sequence based multi lingual low resource speech recognition"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"
    - "enriching word vectors with subword information"
    - "character level incremental speech recognition with recurrent neural networks"
    - "visualizing and understanding recurrent networks"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "first pass large vocabulary continuous speech recognition using bi directional recurrent dnns"

Connected through hyperedge HE-3 (induced by source paper "dialog context aware end to end speech recognition"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "dialog context language modeling with recurrent neural networks"
    - "very deep convolutional networks for end to end speech recognition"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "the microsoft 2016 conversational speech recognition system"
    - "advances in all neural speech recognition"

Connected through hyperedge HE-4 (induced by source paper "neural language codes for multilingual acoustic models"):
  with members:
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"

Connected through hyperedge HE-5 (induced by source paper "subword and crossword units for ctc acoustic models"):
  with members:
    - "advancing acoustic to word ctc model"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "direct acoustics to word models for english conversational speech recognition"
    - "stronger baselines for trustable results in neural machine translation"
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"
    - "acoustic to word model without oov"
    - "advances in all neural 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)