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: "first pass large vocabulary continuous speech recognition using bi directional recurrent dnns"
  Abstract: We present a method to perform first-pass large vocabulary co ntinuous speech recognition using only a neural network and language model. Deep neural network acoustic models are now commonplace in HMM-based speech recognition systems, but building such systems is a complex, domain-specific task. Recent work demonstrated the feasibility of discarding the HMM sequence modeling framework by directly predicting transcript text from audio. This paper extends this approach in two ways. First, we demonstrate that a straightforward recurrent neural network architecture can achieve a high level of accuracy. Second, we propose and evaluate a modified prefix-search decoding algorith m. This approach to decoding enables first-pass speech recognition with a langu age model, completely unaided by the cumbersome infrastructure of HMM-based systems. Experiments on the Wall Street Journal corpus demonstrate fairly competitive word error rates, and the importance of bi-directional network recurrence.

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"
    - "improving the performance of online neural transducer models"
    - "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 "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"
    - "comparison of decoding strategies for ctc acoustic models"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "kt speech crawler automatic dataset construction for speech recognition from youtube videos"):
  with members:
    - "the omg emotion behavior dataset"
    - "mining fine grained opinions on closed captions of youtube videos with an attention rnn"
    - "the kinetics human action video dataset"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep speech scaling up end to end speech recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "a comparison of hybrid and end to end models for syllable recognition"):
  with members:
    - "cold fusion training seq2seq models together with language models"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep speech scaling up end to end speech recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "multi scale alignment and contextual history for attention mechanism in sequence to sequence model"):
  with members:
    - "machine speech chain with one shot speaker adaptation"
    - "online and linear time attention by enforcing monotonic alignments"
    - "listening while speaking speech chain by deep learning"
    - "tacotron towards end to end speech synthesis"
    - "attention based wav2text with feature transfer learning"
    - "local monotonic attention mechanism for end to end speech and language processing"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "end to end feedback loss in speech chain framework via straight through estimator"):
  with members:
    - "multi scale alignment and contextual history for attention mechanism in sequence to sequence model"
    - "machine speech chain with one shot speaker adaptation"
    - "listening while speaking speech chain by deep learning"
    - "tacotron towards end to end speech synthesis"
    - "attention based wav2text with feature transfer learning"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "improving neural machine translation models with monolingual data"

Connected through hyperedge HE-7 (induced by source paper "on the end to end solution to mandarin english code switching speech recognition"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "acoustic and textual data augmentation for improved asr of code switching speech"
    - "unsupervised and efficient vocabulary expansion for recurrent neural network language models in asr"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "improved training of end to end attention models for speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "unsupervised pretraining for sequence to sequence learning"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-8 (induced by source paper "end to end named entity extraction from speech"):
  with members:
    - "towards end to end spoken language understanding"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "named entity recognition with bidirectional lstm cnns"
    - "natural language processing almost from scratch"

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)