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: "sequence to sequence speech recognition with time depth separable convolutions"
  Abstract: We propose a fully convolutional sequence-to-sequence encoder architecture with a simple and efficient decoder. Our model improves WER on LibriSpeech while being an order of magnitude more efficient than a strong RNN baseline. Key to our approach is a time-depth separable convolution block which dramatically reduces the number of parameters in the model while keeping the receptive field large. We also give a stable and efficient beam search inference procedure which allows us to effectively integrate a language model. Coupled with a convolutional language model, our time-depth separable convolution architecture improves by more than 22% relative WER over the best previously reported sequence-to-sequence results on the noisy LibriSpeech test set.

Connected through hyperedge HE-1 (induced by source paper "espresso a fast end to end neural speech recognition toolkit"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "parallelizable stack long short term memory"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "when does label smoothing help"
    - "who needs words lexicon free speech recognition"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "espnet end to end speech processing toolkit"

Connected through hyperedge HE-2 (induced by source paper "language modeling with deep transformers"):
  with members:
    - "on the choice of modeling unit for sequence to sequence speech recognition"
    - "transformer xl attentive language models beyond a fixed length context"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "generating wikipedia by summarizing long sequences"
    - "fully convolutional speech recognition"
    - "self attention with relative position representations"
    - "returnn as a generic flexible neural toolkit with application to translation and speech recognition"
    - "improved training of end to end attention models for speech recognition"

Connected through hyperedge HE-3 (induced by source paper "end to end asr from supervised to semi supervised learning with modern architectures"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "reducing transformer depth on demand with structured dropout"
    - "word level speech recognition with a dynamic lexicon"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "self training for end to end speech recognition"
    - "state of the art speech recognition using multi stream self attention with dilated 1d convolutions"
    - "a fully differentiable beam search decoder"

Connected through hyperedge HE-4 (induced by source paper "self training for end to end speech recognition"):
  with members:
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "lessons from building acoustic models with a million hours of speech"
    - "back translation style data augmentation for end to end asr"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "cycle consistency training for end to end speech recognition"
    - "adversarial training of end to end speech recognition using a criticizing language model"
    - "fully convolutional speech recognition"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-5 (induced by source paper "word level speech recognition with a dynamic lexicon"):
  with members:
    - "who needs words lexicon free speech recognition"
    - "a fully differentiable beam search decoder"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "advancing acoustic to word ctc model"
    - "wav2letter the fastest open source speech recognition system"
    - "improved training of end to end attention models for speech recognition"
    - "minimum word error rate training for attention based sequence to sequence models"
    - "direct acoustics to word models for english conversational speech recognition"

Connected through hyperedge HE-6 (induced by source paper "rwth asr systems for librispeech hybrid vs attention w o data augmentation"):
  with members:
    - "on the choice of modeling unit for sequence to sequence speech recognition"
    - "language modeling with deep transformers"
    - "the capio 2017 conversational speech recognition system"
    - "fully convolutional speech recognition"
    - "returnn as a generic flexible neural toolkit with application to translation and speech recognition"
    - "improved training of end to end attention models for speech recognition"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "optimal completion distillation for sequence learning"

Connected through hyperedge HE-7 (induced by source paper "state of the art speech recognition using multi stream self attention with dilated 1d convolutions"):
  with members:
    - "a comparative study on transformer vs rnn in speech applications"
    - "transformer xl attentive language models beyond a fixed length context"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "the capio 2017 conversational speech recognition system"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a novel pyramidal fsmn architecture with lattice free mmi for speech recognition"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "deja vu double feature presentation and iterated loss in deep transformer networks"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "a comparative study on transformer vs rnn in speech applications"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "fully convolutional speech recognition"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "self attentional acoustic 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)