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: "panns large scale pretrained audio neural networks for audio pattern recognition"
  Abstract: Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification and sound event detection. Recently neural networks have been applied to solve audio pattern recognition problems. However, previous systems focus on small datasets, which limits the performance of audio pattern recognition systems. Recently in computer vision and natural language processing, systems pretrained on large datasets have generalized well to several tasks. However, there is limited research on pretraining neural networks on large datasets for audio pattern recognition. In this paper, we propose large-scale pretrained audio neural networks (PANNs) trained on AudioSet. We propose to use Wavegram, a feature learned from waveform, and the mel spectrogram as input. We investigate the performance and complexity of a variety of convolutional neural networks. Our proposed AudioSet tagging system achieves a state-of-the-art mean average precision (mAP) of 0.439, outperforming the best previous system of 0.392. We transferred a PANN to six audio pattern recognition tasks and achieve state-of-the-art performance in many tasks. Source code and pretrained models have been released.

Connected through hyperedge HE-1 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis"):
  with members:
    - "audio source separation using variational autoencoders and weak class supervision"
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "universal sound separation"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "unsupervised audio source separation via spectrum energy preserved wasserstein learning"
    - "multi scale multi band densenets for audio source separation"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"
    - "single channel audio source separation using convolutional denoising autoencoders"

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)