human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep layered learning in mir"):
  Abstract: Deep learning has boosted the performance of many music information retrieval (MIR) systems in recent years. Yet, the complex hierarchical arrangement of music makes end-to-end learning hard for so ...

Includes members:
  Member "polyphonic pitch tracking with deep layered learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "modeling music modality with a key class invariant pitch chroma cnn")
  Member "onsets and frames dual objective piano transcription":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "music2dance dancenet for music driven dance generation")
  Member "towards playlist generation algorithms using rnns trained on within track transitions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "representation exploration and recommendation of music playlists")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "learning fine grained features via a cnn tree for large scale classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "diversified visual attention networks for fine grained object classification")
  Member "the application of two level attention models in deep convolutional neural network for fine grained image classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition")
  Member "the state of the art ten years after a state of the art future research in music information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis")
  Member "attention for fine grained categorization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition")

Question: Please tell me which class the source paper 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)