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: "frame level instrument recognition by timbre and pitch"
  Abstract: Instrument recognition is a fundamental task in music information retrieval, yet little has been done to predict the presence of instruments in multi-instrument music for each time frame. This task is important for not only automatic transcription but also many retrieval problems. In this paper, we use the newly released MusicNet dataset to study this front, by building and evaluating a convolutional neural network for making frame-level instrument prediction. We consider it as a multi-label classification problem for each frame and use frame-level annotations as the supervisory signal in training the network. Moreover, we experiment with different ways to incorporate pitch information to our model, with the premise that doing so informs the model the notes that are active per frame, and also encourages the model to learn relative rates of energy buildup in the harmonic partials of different instruments. Experiments show salient performance improvement over baseline methods. We also report an analysis probing how pitch information helps the instrument prediction task. Code and experiment details can be found at \url{this https URL}.

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"
    - "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"
    - "audio event detection using weakly labeled data"

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"
    - "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"
    - "sound event detection using spatial features and convolutional recurrent neural network"

Connected through hyperedge HE-3 (induced by source paper "modeling music modality with a key class invariant pitch chroma cnn"):
  with members:
    - "polyphonic pitch tracking with deep layered learning"
    - "a data driven approach to mid level perceptual musical feature modeling"
    - "deep layered learning in mir"
    - "modeling majorness as a perceptual property in music from listener ratings"
    - "genre agnostic key classification with convolutional neural networks"
    - "end to end musical key estimation using a convolutional neural network"
    - "a fully convolutional deep auditory model for musical chord recognition"
    - "feature learning for chord recognition the deep chroma extractor"

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"
    - "multitask learning for frame level instrument recognition"
    - "timbre analysis of music audio signals with convolutional neural networks"
    - "deep convolutional neural networks for predominant instrument recognition in polyphonic music"
    - "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 "performancenet score to audio music generation with multi band convolutional residual network"):
  with members:
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "conditioning deep generative raw audio models for structured automatic music"
    - "singing style transfer using cycle consistent boundary equilibrium generative adversarial networks"
    - "lead sheet generation and arrangement by conditional generative adversarial network"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "progressive growing of gans for improved quality stability and variation"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

Connected through hyperedge HE-6 (induced by source paper "denoising auto encoder with recurrent skip connections and residual regression for music source separation"):
  with members:
    - "an overview of lead and accompaniment separation in music"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "singing style transfer using cycle consistent boundary equilibrium generative adversarial networks"
    - "mad twinnet masker denoiser architecture with twin networks for monaural sound source separation"
    - "vocal melody extraction using patch based cnn"
    - "music source separation using stacked hourglass networks"
    - "transcribing lyrics from commercial song audio the first step towards singing content processing"
    - "multi scale multi band densenets for audio source separation"

Connected through hyperedge HE-7 (induced by source paper "polyphonic pitch tracking with deep layered learning"):
  with members:
    - "deep layered learning in mir"
    - "onsets and frames dual objective piano transcription"
    - "on the potential of simple framewise approaches to piano transcription"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-8 (induced by source paper "multitask learning for frame level instrument recognition"):
  with members:
    - "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"
    - "automatic instrument recognition in polyphonic music using convolutional neural networks"

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)