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: "applying topological persistence in convolutional neural network for music audio signals"
  Abstract: Recent years have witnessed an increased interest in the application of persistent homology, a topological tool for data analysis, to machine learning problems. Persistent homology is known for its ability to numerically characterize the shapes of spaces induced by features or functions. On the other hand, deep neural networks have been shown effective in various tasks. To our best knowledge, however, existing neural network models seldom exploit shape information. In this paper, we investigate a way to use persistent homology in the framework of deep neural networks. Specifically, we propose to embed the so-called "persistence landscape," a rather new topological summary for data, into a convolutional neural network (CNN) for dealing with audio signals. Our evaluation on automatic music tagging, a multi-label classification task, shows that the resulting persistent convolutional neural network (PCNN) model can perform significantly better than state-of-the-art models in prediction accuracy. We also discuss the intuition behind the design of the proposed model, and offer insights into the features that it learns.

Connected through hyperedge HE-1 (induced by source paper "efficient topological layer based on persistent landscapes"):
  with members:
    - "a topology layer for machine learning"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "connectivity optimized representation learning via persistent homology"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "deep sets"
    - "topological approaches to deep learning"
    - "a persistence landscapes toolbox for topological statistics"
    - "deep learning with topological signatures"

Connected through hyperedge HE-2 (induced by source paper "a topology layer for machine learning"):
  with members:
    - "topology of learning in artificial neural networks"
    - "explicit topological priors for deep learning based image segmentation using persistent homology"
    - "adversarial examples target topological holes in deep networks"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "characterizing the shape of activation space in deep neural networks"
    - "exposition and interpretation of the topology of neural networks"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "topology aware surface reconstruction for point clouds"

Connected through hyperedge HE-3 (induced by source paper "topological skeletonization and tree summarization of neurons using discrete morse theory"):
  with members:
    - "graph reconstruction by discrete morse theory"

Connected through hyperedge HE-4 (induced by source paper "sample level cnn architectures for music auto tagging using raw waveforms"):
  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"
    - "timbre analysis of music audio signals with convolutional neural networks"
    - "identity mappings in deep residual networks"
    - "automatic tagging using deep convolutional neural networks"
    - "wide residual networks"
    - "soundnet learning sound representations from unlabeled video"

Connected through hyperedge HE-5 (induced by source paper "sample level deep convolutional neural networks for music auto tagging using raw waveforms"):
  with members:
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "automatic tagging using deep convolutional neural networks"
    - "wavenet a generative model for raw audio"
    - "convolutional recurrent neural networks for music classification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "character aware neural language models"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"):
  with members:
    - "automatic tagging using deep convolutional neural networks"
    - "theano a python framework for fast computation of mathematical expressions"
    - "convolutional recurrent neural networks for music classification"
    - "a deep bag of features model for music auto tagging"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep learning and music adversaries"
    - "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.NE(Neural and Evolutionary Computing)