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: "mosnet deep learning based objective assessment for voice conversion"
  Abstract: Existing objective evaluation metrics for voice conversion (VC) are not always correlated with human perception. Therefore, training VC models with such criteria may not effectively improve naturalness and similarity of converted speech. In this paper, we propose deep learning-based assessment models to predict human ratings of converted speech. We adopt the convolutional and recurrent neural network models to build a mean opinion score (MOS) predictor, termed as MOSNet. The proposed models are tested on large-scale listening test results of the Voice Conversion Challenge (VCC) 2018. Experimental results show that the predicted scores of the proposed MOSNet are highly correlated with human MOS ratings at the system level while being fairly correlated with human MOS ratings at the utterance level. Meanwhile, we have modified MOSNet to predict the similarity scores, and the preliminary results show that the predicted scores are also fairly correlated with human ratings. These results confirm that the proposed models could be used as a computational evaluator to measure the MOS of VC systems to reduce the need for expensive human rating.

Connected through hyperedge HE-1 (induced by source paper "comparison of speech representations for automatic quality estimation in multi speaker text to speech synthesis"):
  with members:
    - "speech replay detection with x vector attack embeddings and spectral features"
    - "embeddings for dnn speaker adaptive training"
    - "libritts a corpus derived from librispeech for text to speech"
    - "analyzing deep cnn based utterance embeddings for acoustic model adaptation"
    - "quality net an end to end non intrusive speech quality assessment model based on blstm"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "automos learning a non intrusive assessor of naturalness of speech"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "moevc a mixture of experts voice conversion system with sparse gating mechanism for accelerating online computation"):
  with members:
    - "deep mixture of experts via shallow embedding"
    - "voxceleb2 deep speaker recognition"
    - "tafe net task aware feature embeddings for efficient learning and inference"
    - "data driven sparse structure selection for deep neural networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "pruning filters for efficient convnets"
    - "unsupervised learning of video representations using lstms"

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)