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 "waveflow a compact flow based model for raw audio"):
  Abstract: In this work, we propose WaveFlow, a small-footprint generative flow for raw audio, which is directly trained with maximum likelihood. WaveFlow handles the long-range structure of waveform with a dilated 2-D convolutional architecture, while modeling the local variations using compact autoregressive functions. It provides a unified view of likelihood-based models for raw audio, including WaveNet and WaveGlow as special cases. WaveFlow can generate high-fidelity speech as WaveNet, while synthesizing several orders of magnitude faster as it only requires a few sequential steps to generate waveforms with hundreds of thousands of time-steps. Furthermore, it can close the significant likelihood gap that has existed between autoregressive models and flow-based models for efficient synthesis. Finally, our small-footprint WaveFlow has 15$\times$ fewer parameters than WaveGlow and can generate 22.05 kHz high-fidelity audio 42.6$\times$ faster than real-time on a V100 GPU without engineered inference kernels.

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 "parallel neural text to speech":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self supervised speech recognition via local prior matching")
  Member "high fidelity speech synthesis with adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "discrete flows invertible generative models of discrete data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the discrepancy between density estimation and sequence generation")
  Member "flow improving flow based generative models with variational dequantization and architecture design":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "woodbury transformations for deep generative flows")
  Member "emerging convolutions for generative normalizing flows":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relational state space model for stochastic multi object systems")
  Member "blow a single scale hyperconditioned flow for non parallel raw audio voice conversion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards robust neural vocoding for speech generation a survey")
  Member "the challenge of realistic music generation modelling raw audio at scale":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hierarchical autoregressive image models with auxiliary decoders")

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)