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: "investigating gated recurrent neural networks for speech synthesis"
  Abstract: Recently, recurrent neural networks (RNNs) as powerful sequence models have re-emerged as a potential acoustic model for statistical parametric speech synthesis (SPSS). The long short-term memory (LSTM) architecture is particularly attractive because it addresses the vanishing gradient problem in standard RNNs, making them easier to train. Although recent studies have demonstrated that LSTMs can achieve significantly better performance on SPSS than deep feed-forward neural networks, little is known about why. Here we attempt to answer two questions: a) why do LSTMs work well as a sequence model for SPSS; b) which component (e.g., input gate, output gate, forget gate) is most important. We present a visual analysis alongside a series of experiments, resulting in a proposal for a simplified architecture. The simplified architecture has significantly fewer parameters than an LSTM, thus reducing generation complexity considerably without degrading quality.

Connected through hyperedge HE-1 (induced by source paper "analyzing learned representations of a deep asr performance prediction model"):
  with members:
    - "asr performance prediction on unseen broadcast programs using convolutional neural networks"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "very deep convolutional neural networks for raw waveforms"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-2 (induced by source paper "minimal gated unit for recurrent neural networks"):
  with members:
    - "lstm a search space odyssey"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "unsupervised learning of video representations using lstms"
    - "phrase based image captioning"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "statistical parametric speech synthesis using bottleneck representation from sequence auto encoder"):
  with members:
    - "lstm a search space odyssey"
    - "multilingual language processing from bytes"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "semi supervised sequence learning"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

Connected through hyperedge HE-4 (induced by source paper "real time interactive sequence generation and control with recurrent neural network ensembles"):
  with members:
    - "lstm a search space odyssey"
    - "generative choreography using deep learning"
    - "deep learning in neural networks"
    - "draw a recurrent neural network for image generation"
    - "learning to execute"
    - "sequence to sequence learning with neural networks"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "fast compact and high quality lstm rnn based statistical parametric speech synthesizers for mobile devices"):
  with members:
    - "loss minimization and parameter estimation with heavy tails"

Connected through hyperedge HE-6 (induced by source paper "improving trajectory modelling for dnn based speech synthesis by using stacked bottleneck features and minimum generation error training"):
  with members:
    - "a high quality text to speech system composed of multiple neural networks"

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)