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: "a comparison on audio signal preprocessing methods for deep neural networks on music tagging"
  Abstract: In this paper, we empirically investigate the effect of audio preprocessing on music tagging with deep neural networks. We perform comprehensive experiments involving audio preprocessing using different time-frequency representations, logarithmic magnitude compression, frequency weighting, and scaling. We show that many commonly used input preprocessing techniques are redundant except magnitude compression.

Connected through hyperedge HE-1 (induced by source paper "music auto tagging using cnns and mel spectrograms with reduced frequency and time resolution"):
  with members:
    - "a tutorial on deep learning for music information retrieval"
    - "the effects of noisy labels on deep convolutional neural networks for music tagging"
    - "multi label music genre classification from audio text and images using deep features"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "end to end learning for music audio tagging at scale"
    - "timbre analysis of music audio signals with convolutional neural networks"
    - "automatic tagging using deep convolutional neural networks"
    - "convolutional recurrent neural networks for music classification"

Connected through hyperedge HE-2 (induced by source paper "one deep music representation to rule them all a comparative analysis of different representation learning strategies"):
  with members:
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "look listen and learn"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "transfer learning for music classification and regression tasks"
    - "one model to learn them all"
    - "deep convolutional neural networks for predominant instrument recognition in polyphonic music"
    - "fma a dataset for music analysis"
    - "cross stitch networks for multi task learning"

Connected through hyperedge HE-3 (induced by source paper "event triggered natural hazard monitoring with convolutional neural networks on the edge"):
  with members:
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "low memory convolutional neural networks through incremental depth first processing"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "hello edge keyword spotting on microcontrollers"
    - "embedded binarized neural networks"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "deep convolutional neural networks and data augmentation for acoustic event detection"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-4 (induced by source paper "a tutorial on deep learning for music information retrieval"):
  with members:
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "began boundary equilibrium generative adversarial networks"
    - "the effects of noisy labels on deep convolutional neural networks for music tagging"
    - "a neural parametric singing synthesizer"
    - "revisiting the problem of audio based hit song prediction using convolutional neural networks"
    - "stacked convolutional and recurrent neural networks for music emotion recognition"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "segan speech enhancement generative adversarial network"

Connected through hyperedge HE-5 (induced by source paper "the effects of noisy labels on deep convolutional neural networks for music tagging"):
  with members:
    - "a tutorial on deep learning for music information retrieval"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "transfer learning for music classification and regression tasks"
    - "deep convolutional neural networks for predominant instrument recognition in polyphonic music"
    - "automatic tagging using deep convolutional neural networks"
    - "convolutional recurrent neural networks for music classification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "dlr toward a deep learned rhythmic representation for music content analysis"):
  with members:
    - "a tutorial on deep learning for music information retrieval"
    - "the effects of noisy labels on deep convolutional neural networks for music tagging"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "feature learning for chord recognition the deep chroma extractor"
    - "automatic tagging using deep convolutional neural networks"
    - "wavenet a generative model for raw audio"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)