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: "musical instrument sound classification with deep convolutional neural network using feature fusion approach"
  Abstract: A new musical instrument classification method using convolutional neural networks (CNNs) is presented in this paper. Unlike the traditional methods, we investigated a scheme for classifying musical instruments using the learned features from CNNs. To create the learned features from CNNs, we not only used a conventional spectrogram image, but also proposed multiresolution recurrence plots (MRPs) that contain the phase information of a raw input signal. Consequently, we fed the characteristic timbre of the particular instrument into a neural network, which cannot be extracted using a phase-blinded representations such as a spectrogram. By combining our proposed MRPs and spectrogram images with a multi-column network, the performance of our proposed classifier system improves over a system that uses only a spectrogram. Furthermore, the proposed classifier also outperforms the baseline result from traditional handcrafted features and classifiers.

Connected through hyperedge HE-1 (induced by source paper "data augmentation for instrument classification robust to audio effects"):
  with members:
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "timbre analysis of music audio signals with convolutional neural networks"
    - "deep convolutional neural networks for predominant instrument recognition in polyphonic music"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "automatic instrument recognition in polyphonic music using convolutional neural networks"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-2 (induced by source paper "timage a robust time series classification pipeline"):
  with members:
    - "insights into lstm fully convolutional networks for time series classification"
    - "deep learning for time series classification a review"
    - "transfer learning for time series classification"
    - "lstm fully convolutional networks for time series classification"
    - "fast and accurate time series classification with weasel"
    - "classification of time series images using deep convolutional neural networks"
    - "imaging time series to improve classification and imputation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "frame level instrument recognition by timbre and pitch"):
  with members:
    - "deep convolutional neural networks for predominant instrument recognition in polyphonic music"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deep convolutional networks on the pitch spiral for musical instrument recognition"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "automatic instrument recognition in polyphonic music using convolutional neural networks"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-4 (induced by source paper "deep convolutional neural networks for predominant instrument recognition in polyphonic music"):
  with members:
    - "understanding neural networks through deep visualization"
    - "recent advances in convolutional neural networks"
    - "automatic instrument recognition in polyphonic music using convolutional neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "empirical evaluation of rectified activations in convolutional network"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"

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)