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: "dlr toward a deep learned rhythmic representation for music content analysis"
  Abstract: In the use of deep neural networks, it is crucial to provide appropriate input representations for the network to learn from. In this paper, we propose an approach to learn a representation that focus on rhythmic representation which is named as DLR (Deep Learning Rhythmic representation). The proposed approach aims to learn DLR from the raw audio signal and use it for other music informatics tasks. A 1-dimensional convolutional network is utilised in the learning of DLR. In the experiment, we present the results from the source task and the target task as well as visualisations of DLRs. The results reveals that DLR provides compact rhythmic information which can be used on multi-tagging task.

Connected through hyperedge HE-1 (induced by source paper "bottom up broadcast neural network for music genre classification"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "randomly weighted cnns for music audio classification"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "audeep unsupervised learning of representations from audio with deep recurrent neural networks"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering"
    - "transfer learning for music classification and regression tasks"

Connected through hyperedge HE-2 (induced by source paper "randomly weighted cnns for music audio classification"):
  with members:
    - "one deep music representation to rule them all a comparative analysis of different representation learning strategies"
    - "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"
    - "look listen and learn"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "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"

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)