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: "automatic instrument recognition in polyphonic music using convolutional neural networks"
  Abstract: Traditional methods to tackle many music information retrieval tasks typically follow a two-step architecture: feature engineering followed by a simple learning algorithm. In these "shallow" architectures, feature engineering and learning are typically disjoint and unrelated. Additionally, feature engineering is difficult, and typically depends on extensive domain expertise. #R##N#In this paper, we present an application of convolutional neural networks for the task of automatic musical instrument identification. In this model, feature extraction and learning algorithms are trained together in an end-to-end fashion. We show that a convolutional neural network trained on raw audio can achieve performance surpassing traditional methods that rely on hand-crafted features.

Connected through hyperedge HE-1 (induced by source paper "learning multi instrument classification with partial labels"):
  with members:
    - "an attention mechanism for musical instrument recognition"
    - "frame level instrument recognition by timbre and pitch"
    - "learning to separate object sounds by watching unlabeled video"
    - "extended playing techniques the next milestone in musical instrument recognition"
    - "focal loss for dense object detection"
    - "audio set classification with attention model a probabilistic perspective"
    - "mixup beyond empirical risk minimization"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-2 (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-3 (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"
    - "deep convolutional neural networks for predominant instrument recognition in polyphonic music"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "musical instrument sound classification with deep convolutional neural network using feature fusion approach"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-4 (induced by source paper "frame level instrument recognition by timbre and pitch"):
  with members:
    - "deep convolutional neural networks for predominant instrument recognition in polyphonic music"
    - "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"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-5 (induced by source paper "multitask learning for frame level instrument recognition"):
  with members:
    - "frame level instrument recognition by timbre and pitch"
    - "revisiting singing voice detection a quantitative review and the future outlook"
    - "weakly supervised visual instrument playing action detection in videos"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "deep convolutional neural networks for predominant instrument recognition in polyphonic music"):
  with members:
    - "understanding neural networks through deep visualization"
    - "recent advances in convolutional neural networks"
    - "musical instrument sound classification with deep convolutional neural network using feature fusion approach"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "empirical evaluation of rectified activations in convolutional network"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "acoustic scene classification using convolutional neural network and multiple width frequency delta data augmentation"):
  with members:
    - "acoustic scene classification classifying environments from the sounds they produce"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "empirical evaluation of rectified activations in convolutional network"
    - "very deep convolutional networks for large scale image recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "network in network"

Connected through hyperedge HE-8 (induced by source paper "deep convolutional networks on the pitch spiral for musical instrument recognition"):
  with members:
    - "deep learning and music adversaries"
    - "adam a method for stochastic optimization"

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)