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: "enabling factorized piano music modeling and generation with the maestro dataset"
  Abstract: Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales. Fortunately, most music is also highly structured and can be represented as discrete note events played on musical instruments. Herein, we show that by using notes as an intermediate representation, we can train a suite of models capable of transcribing, composing, and synthesizing audio waveforms with coherent musical structure on timescales spanning six orders of magnitude (~0.1 ms to ~100 s), a process we call Wave2Midi2Wave. This large advance in the state of the art is enabled by our release of the new MAESTRO (MIDI and Audio Edited for Synchronous TRacks and Organization) dataset, composed of over 172 hours of virtuosic piano performances captured with fine alignment (~3 ms) between note labels and audio waveforms. The networks and the dataset together present a promising approach toward creating new expressive and interpretable neural models of music.

Connected through hyperedge HE-1 (induced by source paper "asmd an automatic framework for compiling multimodal datasets"):
  with members:
    - "multimodal music information processing and retrieval survey and future challenges"
    - "multimodal machine learning a survey and taxonomy"

Connected through hyperedge HE-2 (induced by source paper "toward interpretable music tagging with self attention"):
  with members:
    - "image transformer"
    - "compact generalized non local network"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "nsml meet the mlaas platform with a real world case study"
    - "music transformer"
    - "non local neural networks"
    - "attention is all you need"
    - "onsets and frames dual objective piano transcription"

Connected through hyperedge HE-3 (induced by source paper "adversarial learning for improved onsets and frames music transcription"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "neural music synthesis for flexible timbre control"
    - "generative adversarial networks an overview"
    - "a style based generator architecture for generative adversarial networks"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "mixup beyond empirical risk minimization"
    - "on the potential of simple framewise approaches to piano transcription"

Connected through hyperedge HE-4 (induced by source paper "encoding musical style with transformer autoencoders"):
  with members:
    - "exploring conditioning for generative music systems with human interpretable controls"
    - "learning to groove with inverse sequence transformations"
    - "improving automatic jazz melody generation by transfer learning techniques"
    - "counterpoint by convolution"
    - "this time with feeling learning expressive musical performance"
    - "self attention with relative position representations"
    - "discrete autoencoders for sequence models"
    - "a hierarchical latent vector model for learning long term structure in music"

Connected through hyperedge HE-5 (induced by source paper "learning to groove with inverse sequence transformations"):
  with members:
    - "counterpoint by convolution"
    - "video to video synthesis"
    - "predicting expressive speaking style from text in end to end speech synthesis"
    - "this time with feeling learning expressive musical performance"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "hierarchical neural story generation"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "semi supervised training for improving data efficiency in end to end speech synthesis"

Connected through hyperedge HE-6 (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"
    - "the nes music database a multi instrumental dataset with expressive performance attributes"
    - "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-7 (induced by source paper "computer assisted composition in continuous time"):
  with members:
    - "this time with feeling learning expressive musical performance"
    - "symbolic music data version 1 0"
    - "computer assisted composition with recurrent neural networks"
    - "deepbach a steerable model for bach chorales generation"
    - "ai methods in algorithmic composition a comprehensive survey"

Connected through hyperedge HE-8 (induced by source paper "multitask learning for polyphonic piano transcription a case study"):
  with members:
    - "one deep music representation to rule them all a comparative analysis of different representation learning strategies"
    - "onsets and frames dual objective piano transcription"

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)