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 the rwth extensible training framework for universal recurrent neural networks"
  Abstract: In this work we release our extensible and easily configurable neural network training software. It provides a rich set of functional layers with a particular focus on efficient training of recurrent neural network topologies on multiple GPUs. The source of the software package is public and freely available for academic research purposes and can be used as a framework or as a standalone tool which supports a flexible configuration. The software allows to train state-of-the-art deep bidirectional long short-term memory (LSTM) models on both one dimensional data like speech or two dimensional data like handwritten text and was used to develop successful submission systems in several evaluation campaigns.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "generating synthetic audio data for attention based speech recognition systems"):
  with members:
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "language modeling with deep transformers"
    - "libritts a corpus derived from librispeech for text to speech"
    - "cumulative adaptation for blstm acoustic models"
    - "back translation style data augmentation for end to end asr"
    - "machine speech chain with one shot speaker adaptation"
    - "end to end feedback loss in speech chain framework via straight through estimator"
    - "training neural speech recognition systems with synthetic speech augmentation"

Connected through hyperedge HE-3 (induced by source paper "analysis of deep clustering as preprocessing for automatic speech recognition of sparsely overlapping speech"):
  with members:
    - "the capio 2017 conversational speech recognition system"
    - "end to end monaural multi speaker asr system without pretraining"
    - "a purely end to end system for multi speaker speech recognition"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "deep attractor network for single microphone speaker separation"
    - "the microsoft 2017 conversational speech recognition system"
    - "recognizing multi talker speech with permutation invariant training"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"

Connected through hyperedge HE-4 (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"
    - "returnn as a generic flexible neural toolkit with application to translation and speech recognition"
    - "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"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-5 (induced by source paper "improved training of end to end attention models for speech recognition"):
  with members:
    - "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"
    - "online and linear time attention by enforcing monotonic alignments"
    - "monotonic chunkwise attention"
    - "letter based speech recognition with gated convnets"
    - "state of the art speech recognition with sequence to sequence models"
    - "improving end to end speech recognition with policy learning"
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"

Connected through hyperedge HE-6 (induced by source paper "returnn as a generic flexible neural toolkit with application to translation and speech recognition"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "machine translation at booking com journey and lessons learned"
    - "english conversational telephone speech recognition by humans and machines"
    - "sockeye a toolkit for neural machine translation"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "minimum word error rate training for attention based sequence to sequence models"

Connected through hyperedge HE-7 (induced by source paper "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"):
  with members:
    - "lstm a search space odyssey"
    - "associative long short term memory"
    - "deep learning in neural networks"
    - "deep recurrent neural networks for acoustic modelling"
    - "benchmarking of lstm networks"
    - "deep residual learning for image recognition"
    - "highway long short term memory rnns for distant speech recognition"
    - "training very deep networks"

Connected through hyperedge HE-8 (induced by source paper "a comprehensive study of batch construction strategies for recurrent neural networks in mxnet"):
  with members:
    - "accelerating recurrent neural network training using sequence bucketing and multi gpu data parallelization"
    - "achieving human parity in conversational speech recognition"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

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.LG(Machine Learning)