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: "voice conversion with conditional samplernn"
  Abstract: Here we present a novel approach to conditioning the SampleRNN generative model for voice conversion (VC). Conventional methods for VC modify the perceived speaker identity by converting between source and target acoustic features. Our approach focuses on preserving voice content and depends on the generative network to learn voice style. We first train a multi-speaker SampleRNN model conditioned on linguistic features, pitch contour, and speaker identity using a multi-speaker speech corpus. Voice-converted speech is generated using linguistic features and pitch contour extracted from the source speaker, and the target speaker identity. We demonstrate that our system is capable of many-to-many voice conversion without requiring parallel data, enabling broad applications. Subjective evaluation demonstrates that our approach outperforms conventional VC methods.

Connected through hyperedge HE-1 (induced by source paper "adversarial learning of deepfakes in accounting"):
  with members:
    - "detection of accounting anomalies in the latent space using adversarial autoencoder neural networks"
    - "text based editing of talking head video"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "adversarial text generation via feature mover s distance"
    - "adversarial attacks and defences a survey"
    - "rsgan face swapping and editing using face and hair representation in latent spaces"
    - "everybody dance now"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-2 (induced by source paper "taco vc a single speaker tacotron based voice conversion with limited data"):
  with members:
    - "neural voice cloning with a few samples"
    - "fitting new speakers based on a short untranscribed sample"
    - "voice conversion using sequence to sequence learning of context posterior probabilities"
    - "tacotron towards end to end speech synthesis"
    - "a wavenet for speech denoising"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "statistical parametric speech synthesis incorporating generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "problem agnostic speech embeddings for multi speaker text to speech with samplernn"):
  with members:
    - "learning problem agnostic speech representations from multiple self supervised tasks"
    - "self attention linguistic acoustic decoder"
    - "sample efficient adaptive text to speech"
    - "tacotron towards end to end speech synthesis"
    - "deep voice 2 multi speaker neural text to speech"
    - "samplernn an unconditional end to end neural audio generation model"
    - "wavenet a generative model for raw audio"

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)