human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "towards robust neural vocoding for speech generation a survey"):
  Abstract: Recently, neural vocoders have been widely used in speech synthesis tasks, including text-to-speech and voice conversion. However, in the encounter of data distribution mismatch between training and inference, neural vocoders trained on real data often degrade in voice quality for unseen scenarios. In this paper, we train three commonly used neural vocoders, including WaveNet, WaveRNN, and WaveGlow, alternately on five different datasets. To study the robustness of neural vocoders, we evaluate the models using acoustic features from seen/unseen speakers, seen/unseen languages, a text-to-speech model, and a voice conversion model. In this work, we found that WaveNet is more robust than WaveRNN, especially in the face of inconsistency between training and testing data. Through our experiments, we show that WaveNet is more suitable for text-to-speech models, and WaveRNN more suitable for voice conversion applications. Furthermore, we present results with considerable reference value of subjective human evaluation for future studies.

Includes members:
  Member "fastspeech fast robust and controllable text to speech":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semi supervised neural architecture search")
  Member "blow a single scale hyperconditioned flow for non parallel raw audio voice conversion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "waveflow a compact flow based model for raw audio")
  Member "stargan vc non parallel many to many voice conversion with star generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "voice conversion for whispered speech synthesis")
  Member "waveglow a flow based generative network for speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "videoflow a conditional flow based model for stochastic video generation")
  Member "efficient neural audio synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "what is the state of neural network pruning")
  Member "deep voice 3 scaling text to speech with convolutional sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semi supervised neural architecture search")
  Member "tacotron towards end to end speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "semi supervised neural architecture search")
  Member "deep voice 3 2000 speaker neural text to speech":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "semi supervised neural architecture search")

Question: Please tell me which class the source paper 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)