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: "aishell 1 an open source mandarin speech corpus and a speech recognition baseline"
  Abstract: An open-source Mandarin speech corpus called AISHELL-1 is released. It is by far the largest corpus which is suitable for conducting the speech recognition research and building speech recognition systems for Mandarin. The recording procedure, including audio capturing devices and environments are presented in details. The preparation of the related resources, including transcriptions and lexicon are described. The corpus is released with a Kaldi recipe. Experimental results implies that the quality of audio recordings and transcriptions are promising.

Connected through hyperedge HE-1 (induced by source paper "speaker aware speech transformer"):
  with members:
    - "speaker adaptation for end to end ctc models"
    - "advancing acoustic to word ctc model"
    - "aishell 2 transforming mandarin asr research into industrial scale"
    - "attention is all you need"
    - "embedding based speaker adaptive training of deep neural networks"
    - "end to end attention based large vocabulary speech recognition"
    - "attention based models for speech recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-2 (induced by source paper "universal phone recognition with a multilingual allophone system"):
  with members:
    - "towards zero shot learning for automatic phonemic transcription"
    - "multilingual speech recognition with corpus relatedness sampling"
    - "towards a general purpose linguistic annotation backend"
    - "sequence based multi lingual low resource speech recognition"
    - "thchs 30 a free chinese speech corpus"

Connected through hyperedge HE-3 (induced by source paper "a comparative study on transformer vs rnn in speech applications"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "language modeling with deep transformers"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "ted lium 3 twice as much data and corpus repartition for experiments on speaker adaptation"
    - "scaling neural machine translation"
    - "training tips for the transformer model"
    - "neural speech synthesis with transformer network"

Connected through hyperedge HE-4 (induced by source paper "analyzing asr pretraining for low resource speech to text translation"):
  with members:
    - "fluent translations from disfluent speech in end to end speech translation"
    - "massively multilingual adversarial speech recognition"
    - "tied multitask learning for neural speech translation"
    - "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling"
    - "low resource speech to text translation"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "sequence based multi lingual low resource speech recognition"

Connected through hyperedge HE-5 (induced by source paper "hearing lips improving lip reading by distilling speech recognizers"):
  with members:
    - "a cascade sequence to sequence model for chinese mandarin lip reading"
    - "large scale visual speech recognition"
    - "learning to steer by mimicking features from heterogeneous auxiliary networks"
    - "audio visual speech recognition with a hybrid ctc attention architecture"
    - "smoothgrad removing noise by adding noise"
    - "attention is all you need"
    - "tacotron towards end to end speech synthesis"
    - "combining residual networks with lstms for lipreading"

Connected through hyperedge HE-6 (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-7 (induced by source paper "unsupervised pre training for sequence to sequence speech recognition"):
  with members:
    - "completely unsupervised phoneme recognition by a generative adversarial network harmonized with iteratively refined hidden markov models"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "aishell 2 transforming mandarin asr research into industrial scale"
    - "attention is all you need"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "end to end attention based large vocabulary speech recognition"
    - "attention based models for speech recognition"

Connected through hyperedge HE-8 (induced by source paper "lightweight and efficient end to end speech recognition using low rank transformer"):
  with members:
    - "code switched language models using neural based synthetic data from parallel sentences"
    - "shrinkml end to end asr model compression using reinforcement learning"
    - "on the effectiveness of low rank matrix factorization for lstm model compression"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "attention is all you need"
    - "factorization tricks for lstm networks"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "joint ctc attention based end to end speech recognition using multi task learning"

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)