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: "conditioning deep generative raw audio models for structured automatic music"
  Abstract: Existing automatic music generation approaches that feature deep learning can be broadly classified into two types: raw audio models and symbolic models. Symbolic models, which train and generate at the note level, are currently the more prevalent approach; these models can capture long-range dependencies of melodic structure, but fail to grasp the nuances and richness of raw audio generations. Raw audio models, such as DeepMind's WaveNet, train directly on sampled audio waveforms, allowing them to produce realistic-sounding, albeit unstructured music. In this paper, we propose an automatic music generation methodology combining both of these approaches to create structured, realistic-sounding compositions. We consider a Long Short Term Memory network to learn the melodic structure of different styles of music, and then use the unique symbolic generations from this model as a conditioning input to a WaveNet-based raw audio generator, creating a model for automatic, novel music. We then evaluate this approach by showcasing results of this work.

Connected through hyperedge HE-1 (induced by source paper "mappa mundi an interactive artistic mind map generator with artificial imagination"):
  with members:
    - "from knowledge map to mind map artificial imagination"
    - "image inspired poetry generation in xiaoice"
    - "deep contextualized word representations"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "conditional image generation with pixelcnn decoders"
    - "image to image translation with conditional adversarial networks"
    - "deepbach a steerable model for bach chorales generation"
    - "attribute2image conditional image generation from visual attributes"

Connected through hyperedge HE-2 (induced by source paper "from knowledge map to mind map artificial imagination"):
  with members:
    - "image inspired poetry generation in xiaoice"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "conditional image generation with pixelcnn decoders"
    - "deepbach a steerable model for bach chorales generation"
    - "attribute2image conditional image generation from visual attributes"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "enabling factorized piano music modeling and generation with the maestro dataset"):
  with members:
    - "deep polyphonic adsr piano note transcription"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "attention is all you need"
    - "onsets and frames dual objective piano transcription"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-5 (induced by source paper "deep learning techniques for music generation a survey"):
  with members:
    - "music generation by deep learning challenges and directions"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "deepj style specific music generation"
    - "music style transfer issues a position paper"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "the machine learning algorithm as creative musical tool"
    - "music style transfer a position paper"
    - "music transformer"

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)