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 "ddsp differentiable digital signal processing"):
  Abstract: Most generative models of audio directly generate samples in one of two domains: time or frequency. While sufficient to express any signal, these representations are inefficient, as they do not utilize existing knowledge of how sound is generated and perceived. A third approach (vocoders/synthesizers) successfully incorporates strong domain knowledge of signal processing and perception, but has been less actively researched due to limited expressivity and difficulty integrating with modern auto-differentiation-based machine learning methods. In this paper, we introduce the Differentiable Digital Signal Processing (DDSP) library, which enables direct integration of classic signal processing elements with deep learning methods. Focusing on audio synthesis, we achieve high-fidelity generation without the need for large autoregressive models or adversarial losses, demonstrating that DDSP enables utilizing strong inductive biases without losing the expressive power of neural networks. Further, we show that combining interpretable modules permits manipulation of each separate model component, with applications such as independent control of pitch and loudness, realistic extrapolation to pitches not seen during training, blind dereverberation of room acoustics, transfer of extracted room acoustics to new environments, and transformation of timbre between disparate sources. In short, DDSP enables an interpretable and modular approach to generative modeling, without sacrificing the benefits of deep learning. The library is publicly available at this https URL and we welcome further contributions from the community and domain experts.

Includes members:
  Member "universal audio synthesizer control with normalizing flows":
    also connected through other hyperedges:
      (none)
  Member "wgansing a multi voice singing voice synthesizer based on the wasserstein gan":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sequence to sequence singing synthesis using the feed forward transformer")
  Member "fast spectrogram inversion using multi head convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "single channel speech enhancement using temporal convolutional recurrent neural networks")
  Member "gansynth adversarial neural audio synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on leveraging pretrained gans for limited data generation")
  Member "challenging common assumptions in the unsupervised learning of disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "sing symbol to instrument neural generator":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "score and lyrics free singing voice generation")
  Member "efficient neural audio synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "what is the state of neural network pruning")
  Member "adversarial audio synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "subadditivity of probability divergences on bayes nets with applications to time series gans")

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.LG(Machine Learning)