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: "piano transcription in the studio using an extensible alternating directions framework"
  Abstract: Given a musical audio recording, the goal of automatic music transcription is to determine a score-like representation of the piece underlying the recording. Despite significant interest within the research community, several studies have reported on a “glass ceiling” effect, an apparent limit on the transcription accuracy that current methods seem incapable of overcoming. In this paper, we explore how much this effect can be mitigated by focusing on a specific instrument class and making use of additional information on the recording conditions available in studio or home recording scenarios. In particular, exploiting the availability of single note recordings for the instrument in use, we develop a novel signal model employing variable-length spectro-temporal patterns as its central building blocks-tailored for pitched percussive instruments such as the piano. Temporal dependencies between spectral templates are modeled, resembling characteristics of factorial scaled hidden Markov models FS-HMM and other methods combining nonnegative matrix factorization with Markov processes. In contrast to FS-HMMs, our parameter estimation is developed in a global, relaxed form within the extensible alternating direction method of multipliers framework, which enables the systematic combination of basic regularizers propagating sparsity and local stationarity in note activity with more complex regularizers imposing temporal semantics. The proposed method achieves an f -measure of 93-95% for note onsets on pieces recorded on a Yamaha Disklavier MAPS DB.

Connected through hyperedge HE-1 (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"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "learning to transcribe by ear"):
  with members:
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "reinforcement learning with deep energy based policies"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "an augmented lagrangian method for piano transcription using equal loudness thresholding and lstm based decoding"):
  with members:
    - "on the potential of simple framewise approaches to piano transcription"
    - "adam a method for stochastic optimization"
    - "recurrent neural network regularization"
    - "practical recommendations for gradient based training of deep architectures"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"
    - "variational inference in non negative factorial hidden markov models for efficient audio source separation"

Connected through hyperedge HE-4 (induced by source paper "onsets and frames dual objective piano transcription"):
  with members:
    - "on the potential of simple framewise approaches to piano transcription"
    - "wavenet a generative model for raw audio"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "audio to score alignment of piano music using rnn based automatic music transcription"):
  with members:
    - "on the potential of simple framewise approaches to 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)