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: "listen attend and spell"
  Abstract: We present Listen, Attend and Spell (LAS), a neural network that learns to transcribe speech utterances to characters. Unlike traditional DNN-HMM models, this model learns all the components of a speech recognizer jointly. Our system has two components: a listener and a speller. The listener is a pyramidal recurrent network encoder that accepts filter bank spectra as inputs. The speller is an attention-based recurrent network decoder that emits characters as outputs. The network produces character sequences without making any independence assumptions between the characters. This is the key improvement of LAS over previous end-to-end CTC models. On a subset of the Google voice search task, LAS achieves a word error rate (WER) of 14.1% without a dictionary or a language model, and 10.3% with language model rescoring over the top 32 beams. By comparison, the state-of-the-art CLDNN-HMM model achieves a WER of 8.0%.

Connected through hyperedge HE-1 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language"):
  with members:
    - "how does learning rate decay help modern neural networks"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "state of the art speech recognition using multi stream self attention with dilated 1d convolutions"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "tied multitask learning for neural speech translation"
    - "advancing acoustic to word ctc model"
    - "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning of audio perception for robotics applications learning to project data to t sne umap space"):
  with members:
    - "neuralogram a deep neural network based representation for audio signals"
    - "end to end spoken language translation"
    - "audio linguistic embeddings for spoken sentences"
    - "learning to model aspects of hearing perception using neural loss functions"
    - "neural style transfer for audio spectograms"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "conditional end to end audio transforms"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"

Connected through hyperedge HE-3 (induced by source paper "deep learning for audio signal processing"):
  with members:
    - "piano genie"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "toward domain invariant speech recognition via large scale training"
    - "efficient neural audio synthesis"
    - "deep contextualized word representations"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"

Connected through hyperedge HE-4 (induced by source paper "deep audio visual speech recognition"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "large scale visual speech recognition"
    - "lrs3 ted a large scale dataset for visual speech recognition"
    - "end to end audiovisual speech recognition"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "wav2letter an end to end convnet based speech recognition system"
    - "lipnet end to end sentence level lipreading"

Connected through hyperedge HE-5 (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"
    - "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"
    - "english conversational telephone speech recognition by humans and machines"

Connected through hyperedge HE-6 (induced by source paper "understanding lstm a tutorial into long short term memory recurrent neural networks"):
  with members:
    - "grid long short term memory"
    - "attention based models for speech recognition"
    - "a neural conversational model"
    - "neural turing machines"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "a clockwork rnn"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "prema a predictive multi task scheduling algorithm for preemptible neural processing units"):
  with members:
    - "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "beyond human level accuracy computational challenges in deep learning"
    - "scale sim systolic cnn accelerator simulator"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "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"

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)