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 networks on the pitch spiral for musical instrument recognition"
  Abstract: Musical performance combines a wide range of pitches, nuances, and expressive techniques. Audio-based classification of musical instruments thus requires to build signal representations that are invariant to such transformations. This article investigates the construction of learned convolutional architectures for instrument recognition, given a limited amount of annotated training data. In this context, we benchmark three different weight sharing strategies for deep convolutional networks in the time-frequency domain: temporal kernels; time-frequency kernels; and a linear combination of time-frequency kernels which are one octave apart, akin to a Shepard pitch spiral. We provide an acoustical interpretation of these strategies within the source-filter framework of quasi-harmonic sounds with a fixed spectral envelope, which are archetypal of musical notes. The best classification accuracy is obtained by hybridizing all three convolutional layers into a single deep learning architecture.

Connected through hyperedge HE-1 (induced by source paper "learning the helix topology of musical pitch"):
  with members:
    - "extended playing techniques the next milestone in musical instrument recognition"
    - "wavelet scattering on the pitch spiral"
    - "trainable frontend for robust and far field keyword spotting"

Connected through hyperedge HE-2 (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 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-3 (induced by source paper "extended playing techniques the next milestone in musical instrument recognition"):
  with members:
    - "classification with joint time frequency scattering"
    - "deep convolutional neural networks for predominant instrument recognition in polyphonic music"
    - "joint time frequency scattering for audio classification"
    - "deep scattering spectrum"
    - "a survey on metric learning for feature vectors and structured data"

Connected through hyperedge HE-4 (induced by source paper "a tutorial on deep learning for music information retrieval"):
  with members:
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "began boundary equilibrium generative adversarial networks"
    - "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"
    - "a neural parametric singing synthesizer"
    - "revisiting the problem of audio based hit song prediction using convolutional neural networks"
    - "stacked convolutional and recurrent neural networks for music emotion recognition"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

Connected through hyperedge HE-5 (induced by source paper "lyrics based music genre classification using a hierarchical attention network"):
  with members:
    - "live score following on sheet music images"
    - "hierarchical attention networks"
    - "automatic tagging using deep convolutional neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"
    - "a convolutional neural network for modelling sentences"
    - "distributed representations of words and phrases and their compositionality"

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)