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: "deep convolutional neural networks for predominant instrument recognition in polyphonic music"
  Abstract: Identifying musical instruments in polyphonic music recordings is a challenging but important problem in the field of music information retrieval. It enables music search by instrument, helps recognize musical genres, or can make music transcription easier and more accurate. In this paper, we present a convolutional neural network framework for predominant instrument recognition in real-world polyphonic music. We train our network from fixed-length music excerpts with a single-labeled predominant instrument and estimate an arbitrary number of predominant instruments from an audio signal with a variable length. To obtain the audio-excerpt-wise result, we aggregate multiple outputs from sliding windows over the test audio. In doing so, we investigated two different aggregation methods: one takes the class-wise average followed by normalization, and the other perform temporally local class-wise max-pooling on the output probability prior to averaging and normalization steps to minimize the effect of averaging process suppresses the activation of sporadically appearing instruments. In addition, we conducted extensive experiments on several important factors that affect the performance, including analysis window size, identification threshold, and activation functions for neural networks to find the optimal set of parameters. Our analysis on the instrument-wise performance found that the onset type is a critical factor for recall and precision of each instrument. Using a dataset of 10k audio excerpts from 11 instruments for evaluation, we found that convolutional neural networks are more robust than conventional methods that exploit spectral features and source separation with support vector machines. Experimental results showed that the proposed convolutional network architecture obtained an F1 measure of 0.619 for micro and 0.513 for macro, respectively, achieving 23.1% and 18.8% in performance improvement compared with the state-of-the-art algorithm.

Connected through hyperedge HE-1 (induced by source paper "an attention mechanism for musical instrument recognition"):
  with members:
    - "learning a deep convnet for multi label classification with partial labels"
    - "frame level instrument recognition by timbre and pitch"
    - "multitask learning for frame level instrument recognition"
    - "deep sets"
    - "extended playing techniques the next milestone in musical instrument recognition"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "data augmentation for instrument classification robust to audio effects"):
  with members:
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "timbre analysis of music audio signals with convolutional neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "musical instrument sound classification with deep convolutional neural network using feature fusion approach"
    - "automatic instrument recognition in polyphonic music using convolutional neural networks"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-3 (induced by source paper "visualizing and understanding self attention based music tagging"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "toward interpretable music tagging with self attention"
    - "non local neural networks"
    - "attention is all you need"
    - "end to end learning for music audio tagging at scale"
    - "explaining deep convolutional neural networks on music classification"
    - "why should i trust you explaining the predictions of any classifier"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "augmentation methods on monophonic audio for instrument classification in polyphonic music"):
  with members:
    - "an attention mechanism for musical instrument recognition"
    - "improving singing voice separation using deep u net and wave u net with data augmentation"
    - "learning a joint embedding space of monophonic and mixed music signals for singing voice"
    - "frame level instrument recognition by timbre and pitch"
    - "multitask learning for frame level instrument recognition"
    - "timbre analysis of music audio signals with convolutional neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "one deep music representation to rule them all a comparative analysis of different representation learning strategies"):
  with members:
    - "a comparison on audio signal preprocessing methods for deep neural networks on music tagging"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "look listen and learn"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "transfer learning for music classification and regression tasks"
    - "one model to learn them all"
    - "fma a dataset for music analysis"
    - "cross stitch networks for multi task learning"

Connected through hyperedge HE-6 (induced by source paper "weakly supervised visual instrument playing action detection in videos"):
  with members:
    - "cortexnet a generic network family for robust visual temporal representations"
    - "look listen and learn"
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"
    - "see hear and read deep aligned representations"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "connectionist temporal modeling for weakly supervised action labeling"
    - "audio event detection using weakly labeled data"
    - "youtube 8m a large scale video classification benchmark"

Connected through hyperedge HE-7 (induced by source paper "frame level instrument recognition by timbre and pitch"):
  with members:
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deep convolutional networks on the pitch spiral for musical instrument recognition"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "musical instrument sound classification with deep convolutional neural network using feature fusion approach"
    - "automatic instrument recognition in polyphonic music using convolutional neural networks"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-8 (induced by source paper "extended playing techniques the next milestone in musical instrument recognition"):
  with members:
    - "classification with joint time frequency scattering"
    - "deep convolutional networks on the pitch spiral for musical instrument recognition"
    - "joint time frequency scattering for audio classification"
    - "deep scattering spectrum"
    - "a survey on metric learning for feature vectors and structured data"

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)