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: "extending recurrent neural aligner for streaming end to end speech recognition in mandarin"
  Abstract: End-to-end models have been showing superiority in Automatic Speech Recognition (ASR). At the same time, the capacity of streaming recognition has become a growing requirement for end-to-end models. Following these trends, an encoder-decoder recurrent neural network called Recurrent Neural Aligner (RNA) has been freshly proposed and shown its competitiveness on two English ASR tasks. However, it is not clear if RNA can be further improved and applied to other spoken language. In this work, we explore the applicability of RNA in Mandarin Chinese and present four effective extensions: In the encoder, we redesign the temporal down-sampling and introduce a powerful convolutional structure. In the decoder, we utilize a regularizer to smooth the output distribution and conduct joint training with a language model. On two Mandarin Chinese conversational telephone speech recognition (MTS) datasets, our Extended-RNA obtains promising performance. Particularly, it achieves 27.7% character error rate (CER), which is superior to current state-of-the-art result on the popular HKUST task.

Connected through hyperedge HE-1 (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"
    - "an online attention based model for speech recognition"
    - "aishell 2 transforming mandarin asr research into industrial scale"
    - "improved training of end to end attention models for speech recognition"

Connected through hyperedge HE-2 (induced by source paper "improving transformer based speech recognition using unsupervised pre training"):
  with members:
    - "pre trained language model representations for language generation"
    - "a comparative study on transformer vs rnn in speech applications"
    - "an unsupervised autoregressive model for speech representation learning"
    - "learning problem agnostic speech representations from multiple self supervised tasks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "wav2vec unsupervised pre training for speech recognition"
    - "self attention aligner a latency control end to end model for asr using self attention network and chunk hopping"
    - "roberta a robustly optimized bert pretraining approach"

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"
    - "an online attention based model for speech recognition"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "attention is all you need"
    - "online segment to segment neural transduction"

Connected through hyperedge HE-4 (induced by source paper "self attention aligner a latency control end to end model for asr using self attention network and chunk hopping"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "self attentional acoustic models"
    - "online and linear time attention by enforcing monotonic alignments"
    - "attention is all you need"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "speech recognition with deep recurrent neural networks"
    - "sequence transduction with recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "an online attention based model for speech recognition"):
  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"
    - "monotonic chunkwise attention"
    - "learning hard alignments with variational inference"
    - "exploring neural transducers for end to end speech recognition"
    - "improving the performance of online neural transducer models"
    - "local monotonic attention mechanism for end to end speech and language processing"
    - "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.SD(Sound)