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: "polyphonic pitch tracking with deep layered learning"
  Abstract: This paper presents a polyphonic pitch tracking system able to extract both framewise and note-based estimates from audio. The system uses several artificial neural networks in a deep layered learning setup. First, cascading networks are applied to a spectrogram for framewise fundamental frequency (f0) estimation. A sparse receptive field is learned by the first network and then used as a filter kernel for parameter sharing throughout the system. The f0 activations are connected across time to extract pitch contours. These contours define a framework within which subsequent networks perform onset and offset detection, operating across both time and smaller pitch fluctuations at the same time. As input, the networks use, e.g., variations of latent representations from the f0 estimation network. Finally, incorrect tentative notes are removed one by one in an iterative procedure that allows a network to classify notes within an accurate context. The system was evaluated on four public test sets: MAPS, Bach10, TRIOS, and the MIREX Woodwind quintet, and performed state-of-the-art results for all four datasets. It performs well across all subtasks: f0, pitched onset, and pitched offset tracking.

Connected through hyperedge HE-1 (induced by source paper "modeling music modality with a key class invariant pitch chroma cnn"):
  with members:
    - "frame level instrument recognition by timbre and pitch"
    - "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-2 (induced by source paper "deep polyphonic adsr piano note transcription"):
  with members:
    - "onsets and frames dual objective piano transcription"
    - "madmom a new python audio and music signal processing library"
    - "on the potential of simple framewise approaches to piano transcription"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-3 (induced by source paper "deep layered learning in mir"):
  with members:
    - "onsets and frames dual objective piano transcription"
    - "towards playlist generation algorithms using rnns trained on within track transitions"
    - "deep residual learning for image recognition"
    - "learning fine grained features via a cnn tree for large scale classification"
    - "the application of two level attention models in deep convolutional neural network for fine grained image classification"
    - "the state of the art ten years after a state of the art future research in music information retrieval"
    - "attention for fine grained categorization"
    - "knowledge matters importance of prior information for 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)