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: "the nes music database a multi instrumental dataset with expressive performance attributes"
  Abstract: Existing research on music generation focuses on composition, but often ignores the expressive performance characteristics required for plausible renditions of resultant pieces. In this paper, we introduce the Nintendo Entertainment System Music Database (NES-MDB), a large corpus allowing for separate examination of the tasks of composition and performance. NES-MDB contains thousands of multi-instrumental songs composed for playback by the compositionally-constrained NES audio synthesizer. For each song, the dataset contains a musical score for four instrument voices as well as expressive attributes for the dynamics and timbre of each voice. Unlike datasets comprised of General MIDI files, NES-MDB includes all of the information needed to render exact acoustic performances of the original compositions. Alongside the dataset, we provide a tool that renders generated compositions as NES-style audio by emulating the device's audio processor. Additionally, we establish baselines for the tasks of composition, which consists of learning the semantics of composing for the NES synthesizer, and performance, which involves finding a mapping between a composition and realistic expressive attributes.

Connected through hyperedge HE-1 (induced by source paper "lakhnes improving multi instrumental music generation with cross domain pre training"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "counterpoint by convolution"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "deepj style specific music generation"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "improving polyphonic music models with feature rich encoding"):
  with members:
    - "lakhnes improving multi instrumental music generation with cross domain pre training"
    - "transformer xl attentive language models beyond a fixed length context"
    - "counterpoint by convolution"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "music transformer"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "attention is all you need"
    - "deepbach a steerable model for bach chorales generation"

Connected through hyperedge HE-3 (induced by source paper "cutting music source separation some slakh a dataset to study the impact of training data quality and quantity"):
  with members:
    - "singing voice separation a study on training data"
    - "sdr half baked or well done"
    - "multitask learning for frame level instrument recognition"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "deep clustering discriminative embeddings for segmentation and separation"

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)