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 "sinusoidal wave generating network based on adversarial learning and its application synthesizing frog sounds for data augmentation"):
  Abstract: Simulators that generate observations based on theoretical models can be important tools for development, prediction, and assessment of signal processing algorithms. In order to design these simulators, painstaking effort is required to construct mathematical models according to their application. Complex models are sometimes necessary to represent a variety of real phenomena. In contrast, obtaining synthetic observations from generative models developed from real observations often require much less effort. This paper proposes a generative model based on adversarial learning. Given that observations are typically signals composed of a linear combination of sinusoidal waves and random noises, sinusoidal wave generating networks are first designed based on an adversarial network. Audio waveform generation can then be performed using the proposed network. Several approaches to designing the objective function of the proposed network using adversarial learning are investigated experimentally. In addition, amphibian sound classification is performed using a convolutional neural network trained with real and synthetic sounds. Both qualitative and quantitative results show that the proposed generative model makes realistic signals and is very helpful for data augmentation and data analysis.

Includes members:
  Member "joint alignment of multiple point sets with batch and incremental expectation maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "registration of multi view point sets under the perspective of expectation maximization")
  Member "midinet a convolutional generative adversarial network for symbolic domain music generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "music generation by deep learning challenges and directions")
  Member "began boundary equilibrium generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "generative cooperative net for image generation and data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "report dynamic eye movement matching and visualization tool in neuro gesture")
  Member "the effectiveness of data augmentation in image classification using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural network based reinforcement learning for audio visual gaze control in human robot interaction")
  Member "improved training of wasserstein gans":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "exploring speech enhancement with generative adversarial networks for robust speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improving gans for speech enhancement")

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)