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: "talking drums generating drum grooves with neural networks"
  Abstract: Presented is a method of generating a full drum kit part for a provided kick-drum sequence. A sequence to sequence neural network model used in natural language translation was adopted to encode multiple musical styles and an online survey was developed to test different techniques for sampling the output of the softmax function. The strongest results were found using a sampling technique that drew from the three most probable outputs at each subdivision of the drum pattern but the consistency of output was found to be heavily dependent on style.

Connected through hyperedge HE-1 (induced by source paper "in a silent way communication between ai and improvising musicians beyond sound"):
  with members:
    - "deepdrum an adaptive conditional neural network"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "deep learning techniques for music generation a survey"
    - "temporal convolutional networks for action segmentation and detection"

Connected through hyperedge HE-2 (induced by source paper "morpheus generating structured music with constrained patterns and tension"):
  with members:
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "a functional taxonomy of music generation systems"
    - "ai methods in algorithmic composition a comprehensive survey"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-3 (induced by source paper "adaptive music composition for games"):
  with members:
    - "wavenet a generative model for raw audio"
    - "music transcription modelling and composition using deep learning"

Connected through hyperedge HE-4 (induced by source paper "deepdrum an adaptive conditional neural network"):
  with members:
    - "deep learning techniques for music generation a survey"
    - "music generation with deep learning"
    - "text based lstm networks for automatic music composition"
    - "deepbach a steerable model for bach chorales generation"

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)