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: "an online attention based model for speech recognition"
  Abstract: Attention-based end-to-end models such as Listen, Attend and Spell (LAS), simplify the whole pipeline of traditional automatic speech recognition (ASR) systems and become popular in the field of speech recognition. In previous work, researchers have shown that such architectures can acquire comparable results to state-of-the-art ASR systems, especially when using a bidirectional encoder and global soft attention (GSA) mechanism. However, bidirectional encoder and GSA are two obstacles for real-time speech recognition. In this work, we aim to stream LAS baseline by removing the above two obstacles. On the encoder side, we use a latency-controlled (LC) bidirectional structure to reduce the delay of forward computation. Meanwhile, an adaptive monotonic chunk-wise attention (AMoChA) mechanism is proposed to replace GSA for the calculation of attention weight distribution. Furthermore, we propose two methods to alleviate the huge performance degradation when combining LC and AMoChA. Finally, we successfully acquire an online LAS model, LC-AMoChA, which has only 3.5% relative performance reduction to LAS baseline on our internal Mandarin corpus.

Connected through hyperedge HE-1 (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"
    - "audio visual speech recognition with a hybrid ctc attention architecture"
    - "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"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"

Connected through hyperedge HE-2 (induced by source paper "cif continuous integrate and fire for end to end speech recognition"):
  with members:
    - "transformers with convolutional context for asr"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "self attention aligner a latency control end to end model for asr using self attention network and chunk hopping"
    - "espnet end to end speech processing toolkit"
    - "wav2letter the fastest open source speech recognition system"
    - "aishell 2 transforming mandarin asr research into industrial scale"
    - "improved training of end to end attention models for speech recognition"
    - "extending recurrent neural aligner for streaming end to end speech recognition in mandarin"

Connected through hyperedge HE-3 (induced by source paper "improving generalization of transformer for speech recognition with parallel schedule sampling and relative positional embedding"):
  with members:
    - "parallel scheduled sampling"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "self attention with relative position representations"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "extending recurrent neural aligner for streaming end to end speech recognition in mandarin"
    - "attention is all you need"
    - "online segment to segment neural transduction"

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)