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: "fast compact and high quality lstm rnn based statistical parametric speech synthesizers for mobile devices"
  Abstract: Acoustic models based on long short-term memory recurrent neural networks (LSTM-RNNs) were applied to statistical parametric speech synthesis (SPSS) and showed significant improvements in naturalness and latency over those based on hidden Markov models (HMMs). This paper describes further optimizations of LSTM-RNN-based SPSS for deployment on mobile devices; weight quantization, multi-frame inference, and robust inference using an {\epsilon}-contaminated Gaussian loss function. Experimental results in subjective listening tests show that these optimizations can make LSTM-RNN-based SPSS comparable to HMM-based SPSS in runtime speed while maintaining naturalness. Evaluations between LSTM-RNN- based SPSS and HMM-driven unit selection speech synthesis are also presented.

Connected through hyperedge HE-1 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks"):
  with members:
    - "an optimized recurrent unit for ultra low power keyword spotting"
    - "streaming end to end speech recognition for mobile devices"
    - "donut ctc based query by example keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "automatic grammar augmentation for robust voice command recognition"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "spoken language understanding on the edge"

Connected through hyperedge HE-2 (induced by source paper "durian duration informed attention network for multimodal synthesis"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "text based editing of talking head video"
    - "clarinet parallel wave generation in end to end text to speech"
    - "close to human quality tts with transformer"
    - "efficient neural audio synthesis"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "neural speech synthesis with transformer network"
    - "hierarchical generative modeling for controllable speech synthesis"

Connected through hyperedge HE-3 (induced by source paper "intrinsically sparse long short term memory networks"):
  with members:
    - "the loss landscape of overparameterized neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "snip single shot network pruning based on connection sensitivity"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep lstm for large vocabulary continuous speech recognition"
    - "learning intrinsic sparse structures within long short term memory"
    - "deep rewiring training very sparse deep networks"
    - "factorization tricks for lstm networks"

Connected through hyperedge HE-4 (induced by source paper "chive varying prosody in speech synthesis with a linguistically driven dynamic hierarchical conditional variational network"):
  with members:
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "tacotron towards end to end speech synthesis"
    - "deep voice 3 2000 speaker neural text to speech"
    - "wavenet a generative model for raw audio"
    - "exploring the limits of language modeling"

Connected through hyperedge HE-5 (induced by source paper "patient trajectory prediction in the mimic iii dataset challenges and pitfalls"):
  with members:
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "minimal gated unit for recurrent neural networks"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "how to construct deep recurrent neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-6 (induced by source paper "grow and prune compact fast and accurate lstms"):
  with members:
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "image captioning"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "exploring sparsity in recurrent neural networks"
    - "nest a neural network synthesis tool based on a grow and prune paradigm"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "learning compact recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "sample efficient adaptive text to speech"):
  with members:
    - "neural voice cloning with a few samples"
    - "playing hard exploration games by watching youtube"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "observe and look further achieving consistent performance on atari"
    - "fitting new speakers based on a short untranscribed sample"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "voiceloop voice fitting and synthesis via a phonological loop"

Connected through hyperedge HE-8 (induced by source paper "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"):
  with members:
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "attention is all you need"
    - "deep voice real time neural text to speech"
    - "tacotron towards end to end speech synthesis"
    - "deep voice 3 2000 speaker neural text to speech"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "neural discrete representation 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.SD(Sound)