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: "getting closer to the essence of music the con espressione manifesto"
  Abstract: This text offers a personal and very subjective view on the current situation of Music Information Research (MIR). Motivated by the desire to build systems with a somewhat deeper understanding of music than the ones we currently have, I try to sketch a number of challenges for the next decade of MIR research, grouped around six simple truths about music that are probably generally agreed on but often ignored in everyday research.

Connected through hyperedge HE-1 (induced by source paper "towards explainable music emotion recognition the route via mid level features"):
  with members:
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (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"
    - "frame level instrument recognition by timbre and pitch"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "this time with feeling learning expressive musical performance"):
  with members:
    - "morpheus generating structured music with constrained patterns and tension"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "a functional taxonomy of music generation systems"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "neural translation of musical style"
    - "maximum entropy models for generation of expressive music"
    - "wavenet a generative model for raw audio"
    - "music transcription modelling and composition using deep learning"

Connected through hyperedge HE-4 (induced by source paper "towards deep modeling of music semantics using eeg regularizers"):
  with members:
    - "deep cross modal correlation learning for audio and lyrics in music retrieval"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "representation learning of music using artist labels"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "on the futility of learning complex frame level language models for chord recognition"):
  with members:
    - "a fully convolutional deep auditory model for musical chord recognition"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "feature learning for chord recognition the deep chroma extractor"
    - "sequence transduction with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "from bach to the beatles the simulation of human tonal expectation using ecologically trained predictive models"):
  with members:
    - "on multiplicative integration with recurrent neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "automatic estimation of harmonic tension by distributed representation of chords"):
  with members:
    - "efficient estimation of word representations in vector space"
    - "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)