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 cnn architectures for music auto tagging using raw waveforms"
  Abstract: Recent work has shown that the end-to-end approach using convolutional neural network (CNN) is effective in various types of machine learning tasks. For audio signals, the approach takes raw waveforms as input using an 1-D convolution layer. In this paper, we improve the 1-D CNN architecture for music auto-tagging by adopting building blocks from state-of-the-art image classification models, ResNets and SENets, and adding multi-level feature aggregation to it. We compare different combinations of the modules in building CNN architectures. The results show that they achieve significant improvements over previous state-of-the-art models on the MagnaTagATune dataset and comparable results on Million Song Dataset. Furthermore, we analyze and visualize our model to show how the 1-D CNN operates.

Connected through hyperedge HE-1 (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-2 (induced by source paper "end to end environmental sound classification using a 1d convolutional neural network"):
  with members:
    - "randomly weighted cnns for music audio classification"
    - "end to end speech recognition from the raw waveform"
    - "learning from between class examples for deep sound recognition"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "very deep convolutional neural networks for raw waveforms"
    - "cnn architectures for large scale audio classification"
    - "automatic environmental sound recognition performance versus computational cost"
    - "soundnet learning sound representations from unlabeled video"

Connected through hyperedge HE-3 (induced by source paper "generative autoregressive networks for 3d dancing move synthesis from music"):
  with members:
    - "modeling human motion with quaternion based neural networks"
    - "listen to dance music driven choreography generation using autoregressive encoder decoder network"
    - "one deep music representation to rule them all a comparative analysis of different representation learning strategies"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "text2action generative adversarial synthesis from language to action"
    - "wavenet a generative model for raw audio"
    - "generative choreography using deep learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "deep content user embedding model for music recommendation"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "current challenges and visions in music recommender systems research"
    - "a deep multimodal approach for cold start music recommendation"
    - "representation learning of music using artist labels"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "raw waveform based audio classification using sample level cnn architectures"):
  with members:
    - "squeeze and excitation networks"
    - "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"
    - "deep residual learning for image recognition"
    - "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)