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 "music auto tagging using cnns and mel spectrograms with reduced frequency and time resolution"):
  Abstract: Automatic tagging of music is an important research topic in Music Information Retrieval achieved improvements with advances in deep learning. In particular, many state-of-the-art systems use Convolutional Neural Networks and operate on mel-spectrogram representations of the audio. In this paper, we compare commonly used mel-spectrogram representations and evaluate model performances that can be achieved by reducing the input size in terms of both lesser amount of frequency bands and larger frame rates. We use the MagnaTagaTune dataset for comprehensive performance comparisons and then compare selected configurations on the larger Million Song Dataset. The results of this study can serve researchers and practitioners in their trade-off decision between accuracy of the models, data storage size and training and inference times.

Includes members:
  Member "a tutorial on deep learning for music information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploiting synchronized lyrics and vocal features for music emotion detection")
  Member "the effects of noisy labels on deep convolutional neural networks for music tagging":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wasserstein adversarial regularization war on label noise")
  Member "a comparison on audio signal preprocessing methods for deep neural networks on music tagging":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "one deep music representation to rule them all a comparative analysis of different representation learning strategies")
  Member "multi label music genre classification from audio text and images using deep features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "machine learning for music genre multifaceted review and experimentation with audioset")
  Member "sample level deep convolutional neural networks for music auto tagging using raw waveforms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning for audio signal processing")
  Member "end to end learning for music audio tagging at scale":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "toward interpretable music tagging with self attention")
  Member "timbre analysis of music audio signals with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis")
  Member "automatic tagging using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep learning for audio signal processing")

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.IR(Information Retrieval)