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 "mel spectrogram augmentation for sequence to sequence voice conversion"):
  Abstract: When training the sequence-to-sequence voice conversion model, we need to handle an issue of insufficient data about the number of speech tuples which consist of the same utterance. This study experimentally investigated the effects of Mel-spectrogram augmentation on the sequence-to-sequence voice conversion model. For Mel-spectrogram augmentation, we adopted the policies proposed in SpecAugment. In addition, we propose new policies for more data variations. To find the optimal hyperparameters of augmentation policies for voice conversion, we experimented based on the new metric, namely deformation per deteriorating ratio. We observed the effect of these through experiments based on various sizes of training set and combinations of augmentation policy. In the experimental results, the time axis warping based policies showed better performance than other policies.

Includes members:
  Member "unsupervised singing voice conversion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blow a single scale hyperconditioned flow for non parallel raw audio voice conversion")
  Member "sequence to sequence acoustic modeling for voice conversion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving sequence to sequence voice conversion by adding text supervision")
  Member "sample efficient adaptive text to speech":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dawson a domain adaptive few shot generation framework")
  Member "tacotron towards end to end speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semi supervised neural architecture search")
  Member "is the deconvolution layer the same as a convolutional layer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "farsee net real time semantic segmentation by efficient multi scale context aggregation and feature space super resolution")
  Member "wavenet a generative model for raw audio":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)