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 "clarinet parallel wave generation in end to end text to speech"):
  Abstract: In this work, we propose a new solution for parallel wave generation by WaveNet. In contrast to parallel WaveNet (van den Oord et al., 2018), we distill a Gaussian inverse autoregressive flow from the autoregressive WaveNet by minimizing a regularized KL divergence between their highly-peaked output distributions. Our method computes the KL divergence in closed-form, which simplifies the training algorithm and provides very efficient distillation. In addition, we introduce the first text-to-wave neural architecture for speech synthesis, which is fully convolutional and enables fast end-to-end training from scratch. It significantly outperforms the previous pipeline that connects a text-to-spectrogram model to a separately trained WaveNet (Ping et al., 2018). We also successfully distill a parallel waveform synthesizer conditioned on the hidden representation in this end-to-end model.

Includes members:
  Member "deterministic non autoregressive neural sequence modeling by iterative refinement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non autoregressive neural dialogue generation")
  Member "fast decoding in sequence models using discrete latent variables":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lava nat a non autoregressive translation model with look around decoding and vocabulary attention")
  Member "theory and experiments on vector quantized autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "minimizing the bag of ngrams difference for non autoregressive neural machine translation")
  Member "deep voice 3 scaling text to speech with convolutional sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semi supervised neural architecture search")
  Member "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive density estimation for generative models")
  Member "parallel wavenet fast high fidelity speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models")
  Member "non autoregressive neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "non autoregressive neural dialogue generation")
  Member "deep voice real time neural text to speech":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unpaired image to speech synthesis with multimodal information bottleneck")

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.CL(Computation and Language)