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: "returnn as a generic flexible neural toolkit with application to translation and speech recognition"
  Abstract: We compare the fast training and decoding speed of RETURNN of attention models for translation, due to fast CUDA LSTM kernels, and a fast pure TensorFlow beam search decoder. We show that a layer-wise pretraining scheme for recurrent attention models gives over 1% BLEU improvement absolute and it allows to train deeper recurrent encoder networks. Promising preliminary results on max. expected BLEU training are presented. We are able to train state-of-the-art models for translation and end-to-end models for speech recognition and show results on WMT 2017 and Switchboard. The flexibility of RETURNN allows a fast research feedback loop to experiment with alternative architectures, and its generality allows to use it on a wide range of applications.

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"
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "when does label smoothing help"
    - "who needs words lexicon free speech recognition"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"

Connected through hyperedge HE-2 (induced by source paper "a comparative study on end to end speech to text translation"):
  with members:
    - "attention passing models for robust and data efficient end to end speech translation"
    - "on using 2d sequence to sequence models for speech recognition"
    - "end to end speech translation with knowledge distillation"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "towards unsupervised speech to text translation"
    - "towards two dimensional sequence to sequence model in neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "low resource speech to text translation"

Connected through hyperedge HE-3 (induced by source paper "language modeling with deep transformers"):
  with members:
    - "on the choice of modeling unit for sequence to sequence speech recognition"
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "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"
    - "improved training of end to end attention models for speech recognition"

Connected through hyperedge HE-4 (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"
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "language modeling with deep transformers"
    - "the capio 2017 conversational speech recognition system"
    - "fully convolutional 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-5 (induced by source paper "on using 2d sequence to sequence models for speech recognition"):
  with members:
    - "towards two dimensional sequence to sequence model in neural machine translation"
    - "improved training of end to end attention models for speech recognition"
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "advances in all neural speech recognition"
    - "end to end attention based large vocabulary speech recognition"
    - "neural machine translation of rare words with subword units"
    - "grid long short term memory"
    - "attention based models for speech recognition"

Connected through hyperedge HE-6 (induced by source paper "cat crf based asr toolkit"):
  with members:
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "espnet end to end speech processing toolkit"
    - "fully convolutional speech recognition"
    - "wav2letter the fastest open source speech recognition system"
    - "improved training of end to end attention models for speech recognition"
    - "monotonic chunkwise attention"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "wav2letter an end to end convnet based speech recognition system"

Connected through hyperedge HE-7 (induced by source paper "shrinkml end to end asr model compression using reinforcement learning"):
  with members:
    - "streaming end to end speech recognition for mobile devices"
    - "federated learning strategies for improving communication efficiency"
    - "amc automl for model compression and acceleration on mobile devices"
    - "improved training of end to end attention models for speech recognition"
    - "monotonic chunkwise attention"
    - "neural architecture search with reinforcement learning"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-8 (induced by source paper "uniblock scoring and filtering corpus with unicode block information"):
  with members:
    - "on the impact of various types of noise on neural machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "a call for clarity in reporting bleu scores"
    - "dual conditional cross entropy filtering of noisy parallel corpora"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "returnn the rwth extensible training framework for universal recurrent neural networks"
    - "bag of tricks for efficient text classification"

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.NE(Neural and Evolutionary Computing)