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: "lead sheet generation and arrangement by conditional generative adversarial network"
  Abstract: Research on automatic music generation has seen great progress due to the development of deep neural networks. However, the generation of multi-instrument music of arbitrary genres still remains a challenge. Existing research either works on lead sheets or multi-track piano-rolls found in MIDIs, but both musical notations have their limits. In this work, we propose a new task called lead sheet arrangement to avoid such limits. A new recurrent convolutional generative model for the task is proposed, along with three new symbolic-domain harmonic features to facilitate learning from unpaired lead sheets and MIDIs. Our model can generate lead sheets and their arrangements of eight-bar long. Audio samples of the generated result can be found at this https URL

Connected through hyperedge HE-1 (induced by source paper "rhythm chord and melody generation for lead sheets using recurrent neural networks"):
  with members:
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "chord generation from symbolic melody using blstm networks"
    - "sampling variations of lead sheets"
    - "deep learning techniques for music generation a survey"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "automatic melody harmonization with triad chords a comparative study"):
  with members:
    - "generative statistical models with self emergent grammar of chord sequences"
    - "chord generation from symbolic melody using blstm networks"
    - "deep learning techniques for music generation a survey"
    - "ai methods in algorithmic composition a comprehensive survey"

Connected through hyperedge HE-3 (induced by source paper "high level control of drum track generation using learned patterns of rhythmic interaction"):
  with members:
    - "audio style transfer"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "deepj style specific music generation"
    - "learning transposition invariant interval features from symbolic music and audio"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "a predictive model for music based on learned interval representations"
    - "self normalizing neural networks"
    - "deep learning techniques for music generation a survey"

Connected through hyperedge HE-4 (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"
    - "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"

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)