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 deep bag of features model for music auto tagging"
  Abstract: Feature learning and deep learning have drawn great attention in recent years as a way of transforming input data into more effective representations using learning algorithms. Such interest has grown in the area of music information retrieval (MIR) as well, particularly in music audio classification tasks such as auto-tagging. In this paper, we present a two-stage learning model to effectively predict multiple labels from music audio. The first stage learns to project local spectral patterns of an audio track onto a high-dimensional sparse space in an unsupervised manner and summarizes the audio track as a bag-of-features. The second stage successively performs the unsupervised learning on the bag-of-features in a layer-by-layer manner to initialize a deep neural network and finally fine-tunes it with the tag labels. Through the experiment, we rigorously examine training choices and tuning parameters, and show that the model achieves high performance on Magnatagatune, a popularly used dataset in music auto-tagging.

Connected through hyperedge HE-1 (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"
    - "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"
    - "feature pyramid networks for object detection"
    - "automatic tagging using deep convolutional neural networks"
    - "learning multiscale features directly from waveforms"

Connected through hyperedge HE-2 (induced by source paper "lukthung classification using neural networks on lyrics and audios"):
  with members:
    - "lyrics based music genre classification using a hierarchical attention network"
    - "end to end learning for music audio tagging at scale"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"):
  with members:
    - "applying topological persistence in convolutional neural network for music audio signals"
    - "automatic tagging using deep convolutional neural networks"
    - "theano a python framework for fast computation of mathematical expressions"
    - "convolutional recurrent neural networks for music classification"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "automatic tagging using deep convolutional neural networks"):
  with members:
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep karaoke extracting vocals from musical mixtures using a convolutional deep neural network"
    - "adam a method for stochastic optimization"
    - "theano new features and speed improvements"

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.LG(Machine Learning)