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: "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
  Abstract: We investigate training end-to-end speech recognition models with the recurrent neural network transducer (RNN-T): a streaming, all-neural, sequence-to-sequence architecture which jointly learns acoustic and language model components from transcribed acoustic data. We explore various model architectures and demonstrate how the model can be improved further if additional text or pronunciation data are available. The model consists of an `encoder', which is initialized from a connectionist temporal classification-based (CTC) acoustic model, and a `decoder' which is partially initialized from a recurrent neural network language model trained on text data alone. The entire neural network is trained with the RNN-T loss and directly outputs the recognized transcript as a sequence of graphemes, thus performing end-to-end speech recognition. We find that performance can be improved further through the use of sub-word units (`wordpieces') which capture longer context and significantly reduce substitution errors. The best RNN-T system, a twelve-layer LSTM encoder with a two-layer LSTM decoder trained with 30,000 wordpieces as output targets achieves a word error rate of 8.5\% on voice-search and 5.2\% on voice-dictation tasks and is comparable to a state-of-the-art baseline at 8.3\% on voice-search and 5.4\% voice-dictation.

Connected through hyperedge HE-1 (induced by source paper "accelerating rnn transducer inference via one step constrained beam search"):
  with members:
    - "rnn t for latency controlled asr with improved beam search"
    - "vectorization of hypotheses and speech for faster beam search in encoder decoder based speech recognition"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "advancing acoustic to word ctc model"
    - "streaming end to end speech recognition for mobile devices"
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "state of the art speech recognition with sequence to sequence models"

Connected through hyperedge HE-2 (induced by source paper "end to end automatic speech recognition integrated with ctc based voice activity detection"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "streaming end to end speech recognition for mobile devices"
    - "dialog context aware end to end speech recognition"
    - "exploring rnn transducer for chinese speech recognition"
    - "an online attention based model for speech recognition"
    - "audio visual speech recognition with a hybrid ctc attention architecture"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "rnn transducer with language bias for end to end mandarin english code switching speech recognition"):
  with members:
    - "joint speech recognition and speaker diarization via sequence transduction"
    - "on the end to end solution to mandarin english code switching speech recognition"
    - "study of semi supervised approaches to improving english mandarin code switching speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "streaming small footprint keyword spotting using sequence to sequence models"
    - "challenges of computational processing of code switching"
    - "end to end attention based large vocabulary speech recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "acoustic to word models with conversational context information"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "advancing acoustic to word ctc model"
    - "hierarchical multi task learning with ctc"
    - "dialog context aware end to end speech recognition"
    - "multi modal data augmentation for end to end asr"
    - "improved training of end to end attention models for speech recognition"
    - "english conversational telephone speech recognition by humans and machines"
    - "building competitive direct acoustics to word models for english conversational speech recognition"

Connected through hyperedge HE-5 (induced by source paper "learned in speech recognition contextual acoustic word embeddings"):
  with members:
    - "advancing acoustic to word ctc model"
    - "deep contextualized word representations"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "learning acoustic word embeddings with temporal context for query by example speech search"
    - "how2 a large scale dataset for multimodal language understanding"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "improved training of end to end attention models for speech recognition"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-6 (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"
    - "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"
    - "no need for a lexicon evaluating the value of the pronunciation lexica in end to end models"

Connected through hyperedge HE-7 (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"
    - "monotonic chunkwise attention"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "exploring neural transducers for end to end speech recognition"
    - "improving the performance of online neural transducer models"

Connected through hyperedge HE-8 (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"
    - "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"
    - "improving the performance of online neural transducer models"
    - "learning hidden unit contributions for unsupervised acoustic model adaptation"

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)