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: "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
  Abstract: Recent experiments show that deep bidirectional long short-term memory (BLSTM) recurrent neural network acoustic models outperform feedforward neural networks for automatic speech recognition (ASR). However, their training requires a lot of tuning and experience. In this work, we provide a comprehensive overview over various BLSTM training aspects and their interplay within ASR, which has been missing so far in the literature. We investigate on different variants of optimization methods, batching, truncated backpropagation, and regularization techniques such as dropout, and we study the effect of size and depth, training models of up to 10 layers. This includes a comparison of computation times vs. recognition performance. Furthermore, we introduce a pretraining scheme for LSTMs with layer-wise construction of the network showing good improvements especially for deep networks. The experimental analysis mainly was performed on the Quaero task, with additional results on Switchboard. The best BLSTM model gave a relative improvement in word error rate of over 15% compared to our best feed-forward baseline on our Quaero 50h task. All experiments were done using RETURNN and RASR, RWTH's extensible training framework for universal recurrent neural networks and ASR toolkit. The training configuration files are publicly available.

Connected through hyperedge HE-1 (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"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "state of the art speech recognition with sequence to sequence models"

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"
    - "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"
    - "returnn as a generic flexible neural toolkit with application to translation and speech recognition"

Connected through hyperedge HE-3 (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-4 (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"
    - "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"
    - "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-5 (induced by source paper "svgd a virtual gradients descent method for stochastic optimization"):
  with members:
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "aggregated residual transformations for deep neural networks"
    - "tensorflow a system for large scale machine learning"
    - "neural random access machines"
    - "neural programmer inducing latent programs with gradient descent"
    - "deep residual learning for image recognition"
    - "automatic differentiation in machine learning a survey"
    - "neural gpus learn algorithms"

Connected through hyperedge HE-6 (induced by source paper "understanding feature selection and feature memorization in recurrent neural networks"):
  with members:
    - "opening the black box of deep neural networks via information"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "neural summarization by extracting sentences and words"
    - "neural machine translation in linear time"
    - "dizzyrnn reparameterizing recurrent neural networks for norm preserving backpropagation"
    - "unitary evolution recurrent neural networks"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-7 (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"
    - "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"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"

Connected through hyperedge HE-8 (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"
    - "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"
    - "classical structured prediction losses for sequence to sequence 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.NE(Neural and Evolutionary Computing)