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 tutorial on deep learning for music information retrieval"
  Abstract: Following their success in Computer Vision and other areas, deep learning techniques have recently become widely adopted in Music Information Retrieval (MIR) research. However, the majority of works aim to adopt and assess methods that have been shown to be effective in other domains, while there is still a great need for more original research focusing on music primarily and utilising musical knowledge and insight. The goal of this paper is to boost the interest of beginners by providing a comprehensive tutorial and reducing the barriers to entry into deep learning for MIR. We lay out the basic principles and review prominent works in this hard to navigate field. We then outline the network structures that have been successful in MIR problems and facilitate the selection of building blocks for the problems at hand. Finally, guidelines for new tasks and some advanced topics in deep learning are discussed to stimulate new research in this fascinating field.

Connected through hyperedge HE-1 (induced by source paper "exploiting synchronized lyrics and vocal features for music emotion detection"):
  with members:
    - "convolutional attention networks for multimodal emotion recognition from speech and text data"
    - "end to end music source separation is it possible in the waveform domain"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "music mood detection based on audio and lyrics with deep neural net"
    - "attention is all you need"
    - "kapre on gpu audio preprocessing layers for a quick implementation of deep neural network models with keras"

Connected through hyperedge HE-2 (induced by source paper "music auto tagging using cnns and mel spectrograms with reduced frequency and time resolution"):
  with members:
    - "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"
    - "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"
    - "automatic tagging using deep convolutional neural networks"
    - "convolutional recurrent neural networks for music classification"

Connected through hyperedge HE-3 (induced by source paper "jtav jointly learning social media content representation by fusing textual acoustic and visual features"):
  with members:
    - "image2song song retrieval via bridging image content and lyric words"
    - "visual and textual sentiment analysis using deep fusion convolutional neural networks"
    - "image text multi modal representation learning by adversarial backpropagation"
    - "enriching word vectors with subword information"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "social media image analysis for public health"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "raw multi channel audio source separation using multi resolution convolutional auto encoders"):
  with members:
    - "does phase matter for monaural source separation"
    - "single channel audio source separation using convolutional denoising autoencoders"
    - "time frequency trade offs for audio source separation with binary masks"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "deep learning techniques for music generation a survey"):
  with members:
    - "music generation by deep learning challenges and directions"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "conditioning deep generative raw audio models for structured automatic music"
    - "deepj style specific music generation"
    - "music style transfer issues a position paper"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "the machine learning algorithm as creative musical tool"
    - "music style transfer a position paper"

Connected through hyperedge HE-6 (induced by source paper "the effects of noisy labels on deep convolutional neural networks for music tagging"):
  with members:
    - "a comparison on audio signal preprocessing methods for deep neural networks on music tagging"
    - "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"
    - "transfer learning for music classification and regression tasks"
    - "deep convolutional neural networks for predominant instrument recognition in polyphonic music"
    - "automatic tagging using deep convolutional neural networks"
    - "convolutional recurrent neural networks for music classification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "dlr toward a deep learned rhythmic representation for music content analysis"):
  with members:
    - "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"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "feature learning for chord recognition the deep chroma extractor"
    - "automatic tagging using deep convolutional neural networks"
    - "wavenet a generative model for raw audio"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "a comparison on audio signal preprocessing methods for deep neural networks on music tagging"):
  with members:
    - "kapre on gpu audio preprocessing layers for a quick implementation of deep neural network models with keras"
    - "understanding how image quality affects deep neural networks"
    - "automatic tagging using deep convolutional neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "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.CV(Computer Vision and Pattern Recognition)