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: "state of the art speech recognition with sequence to sequence models"
  Abstract: Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS), subsume the acoustic, pronunciation and language model components of a traditional automatic speech recognition (ASR) system into a single neural network. In previous work, we have shown that such architectures are comparable to state-of-theart ASR systems on dictation tasks, but it was not clear if such architectures would be practical for more challenging tasks such as voice search. In this work, we explore a variety of structural and optimization improvements to our LAS model which significantly improve performance. On the structural side, we show that word piece models can be used instead of graphemes. We also introduce a multi-head attention architecture, which offers improvements over the commonly-used single-head attention. On the optimization side, we explore synchronous training, scheduled sampling, label smoothing, and minimum word error rate optimization, which are all shown to improve accuracy. We present results with a unidirectional LSTM encoder for streaming recognition. On a 12, 500 hour voice search task, we find that the proposed changes improve the WER from 9.2% to 5.6%, while the best conventional system achieves 6.7%; on a dictation task our model achieves a WER of 4.1% compared to 5% for the conventional system.

Connected through hyperedge HE-1 (induced by source paper "towards efficient model compression via learned global ranking"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "regularizing activation distribution for training binarized deep networks"
    - "adascale towards real time video object detection using adaptive scaling"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "importance estimation for neural network pruning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mavbench micro aerial vehicle benchmarking"

Connected through hyperedge HE-2 (induced by source paper "stavis spatio temporal audiovisual saliency network"):
  with members:
    - "dave a deep audio visual embedding for dynamic saliency prediction"
    - "the sound of motions"
    - "learning to localize sound source in visual scenes"
    - "deep visual attention prediction"
    - "deepvs a deep learning based video saliency prediction approach"
    - "audio visual event localization in unconstrained videos"
    - "end to end saliency mapping via probability distribution prediction"
    - "susinet see understand and summarize it"

Connected through hyperedge HE-3 (induced by source paper "low rank bottleneck in multi head attention models"):
  with members:
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "adaptively sparse transformers"
    - "pay less attention with lightweight and dynamic convolutions"
    - "are transformers universal approximators of sequence to sequence functions"
    - "token level ensemble distillation for grapheme to phoneme conversion"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "universal transformers"

Connected through hyperedge HE-4 (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-5 (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"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "exploiting event driven cameras for spatio temporal prediction of fast changing trajectories"):
  with members:
    - "continuous time visual inertial odometry for event cameras"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "unsupervised learning of dense optical flow and depth from sparse event data"
    - "lstm based network for human gait stability prediction in an intelligent robotic rollator"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "real time 6dof pose relocalization for event cameras with stacked spatial lstm networks"
    - "ddd17 end to end davis driving dataset"
    - "sequence to sequence video to text"

Connected through hyperedge HE-7 (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"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "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-8 (induced by source paper "classification of hand movements from eeg using a deep attention based lstm network"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "lstm a search space odyssey"
    - "regularizing and optimizing lstm language models"
    - "eeg based intention recognition from spatio temporal representations via cascade and parallel convolutional recurrent neural networks"
    - "a critical review of recurrent neural networks for sequence learning"
    - "adam a method for stochastic optimization"
    - "automated classification of l r hand movement eeg signals using advanced feature extraction and machine 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)