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: "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
  Abstract: Recently, the end-to-end approach that learns hierarchical representations from raw data using deep convolutional neural networks has been successfully explored in the image, text and speech domains. This approach was applied to musical signals as well but has been not fully explored yet. To this end, we propose sample-level deep convolutional neural networks which learn representations from very small grains of waveforms (e.g. 2 or 3 samples) beyond typical frame-level input representations. Our experiments show how deep architectures with sample-level filters improve the accuracy in music auto-tagging and they provide results comparable to previous state-of-the-art performances for the Magnatagatune dataset and Million Song Dataset. In addition, we visualize filters learned in a sample-level DCNN in each layer to identify hierarchically learned features and show that they are sensitive to log-scaled frequency along layer, such as mel-frequency spectrogram that is widely used in music classification systems.

Connected through hyperedge HE-1 (induced by source paper "deep learning for audio signal processing"):
  with members:
    - "piano genie"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "toward domain invariant speech recognition via large scale training"
    - "efficient neural audio synthesis"
    - "deep contextualized word representations"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"

Connected through hyperedge HE-2 (induced by source paper "toward interpretable music tagging with self attention"):
  with members:
    - "image transformer"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "compact generalized non local network"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "nsml meet the mlaas platform with a real world case study"
    - "music transformer"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "panns large scale pretrained audio neural networks for audio pattern recognition"):
  with members:
    - "musicnn pre trained convolutional neural networks for music audio tagging"
    - "bottom up broadcast neural network for music genre classification"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "weakly labelled audioset tagging with attention neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "j net randomly weighted u net for audio source separation"):
  with members:
    - "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation"
    - "progressive image deraining networks a better and simpler baseline"
    - "convolutional recurrent neural network based progressive learning for monaural speech enhancement"
    - "audio denoising with deep network priors"
    - "randomly weighted cnns for music audio classification"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "deep image prior"

Connected through hyperedge HE-5 (induced by source paper "on the performance of residual block design alternatives in convolutional neural networks for end to end audio classification"):
  with members:
    - "acoustic scene classification using multi layer temporal pooling based on convolutional neural network"
    - "a report on sound event detection with different binaural features"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "very deep convolutional neural networks for raw waveforms"
    - "identity mappings in deep residual networks"
    - "xception deep learning with depthwise separable convolutions"
    - "understanding audio pattern using convolutional neural network from raw waveforms"

Connected through hyperedge HE-6 (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"
    - "a comparison on audio signal preprocessing methods for deep neural networks on music tagging"
    - "multi label music genre classification from audio text and images using deep features"
    - "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-7 (induced by source paper "multi scale embedded cnn for music tagging mse cnn"):
  with members:
    - "panoptic feature pyramid networks"
    - "feature pyramid network for multi class land segmentation"
    - "end to end learning for music audio tagging at scale"
    - "timbre analysis of music audio signals with convolutional neural networks"
    - "feature pyramid networks for object detection"
    - "automatic tagging using deep convolutional neural networks"
    - "learning multiscale features directly from waveforms"
    - "convolutional recurrent neural networks for music classification"

Connected through hyperedge HE-8 (induced by source paper "are nearby neighbors relatives diagnosing deep music embedding spaces"):
  with members:
    - "raw waveform based audio classification using sample level cnn architectures"
    - "adversarial examples in the physical world"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep learning and music adversaries"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)