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: "madmom a new python audio and music signal processing library"
  Abstract: In this paper, we present madmom, an open-source audio processing and music information retrieval (MIR) library written in Python. madmom features a concise, NumPy-compatible, object oriented design with simple calling conventions and sensible default values for all parameters, which facilitates fast prototyping of MIR applications. Prototypes can be seamlessly converted into callable processing pipelines through madmom's concept of Processors, callable objects that run transparently on multiple cores. Processors can also be serialised, saved, and re-run to allow results to be easily reproduced anywhere. Apart from low-level audio processing, madmom puts emphasis on musically meaningful high-level features. Many of these incorporate machine learning techniques and madmom provides a module that implements some in MIR commonly used methods such as hidden Markov models and neural networks. Additionally, madmom comes with several state-of-the-art MIR algorithms for onset detection, beat, downbeat and meter tracking, tempo estimation, and piano transcription. These can easily be incorporated into bigger MIR systems or run as stand-alone programs.

Connected through hyperedge HE-1 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis"):
  with members:
    - "predicting time to failure of plasma etching equipment using machine learning"
    - "cross modal music retrieval and applications an overview of key methodologies"
    - "multi task music representation learning from multi label embeddings"
    - "fashion and apparel classification using convolutional neural networks"
    - "a multi modal deep neural network approach to bird song identification"
    - "large scale audio visual video analytics platform for forensic investigations of terroristic attacks"
    - "multi temporal resolution convolutional neural networks for acoustic scene classification"
    - "content based video music retrieval using soft intra modal structure constraint"

Connected through hyperedge HE-2 (induced by source paper "music2dance dancenet for music driven dance generation"):
  with members:
    - "dancing to music"
    - "learning individual styles of conversational gesture"
    - "a neural temporal model for human motion prediction"
    - "listen to dance music driven choreography generation using autoregressive encoder decoder network"
    - "convolutional sequence to sequence model for human dynamics"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "weakly supervised deep recurrent neural networks for basic dance step generation"
    - "onsets and frames dual objective piano transcription"

Connected through hyperedge HE-3 (induced by source paper "music2dance music driven dance generation using wavenet"):
  with members:
    - "a neural temporal model for human motion prediction"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "listen to dance music driven choreography generation using autoregressive encoder decoder network"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "weakly supervised deep recurrent neural networks for basic dance step generation"
    - "onsets and frames dual objective piano transcription"
    - "on human motion prediction using recurrent neural networks"
    - "auto conditioned recurrent networks for extended complex human motion synthesis"

Connected through hyperedge HE-4 (induced by source paper "deep learning for mir tutorial"):
  with members:
    - "fashion and apparel classification using convolutional neural networks"
    - "a multi modal deep neural network approach to bird song identification"

Connected through hyperedge HE-5 (induced by source paper "score and lyrics free singing voice generation"):
  with members:
    - "dilated convolution with dilated gru for music source separation"
    - "data efficient voice cloning for neural singing synthesis"
    - "high fidelity speech synthesis with adversarial networks"
    - "adversarially trained end to end korean singing voice synthesis system"
    - "gansynth adversarial neural audio synthesis"
    - "an overview of lead and accompaniment separation in music"
    - "group normalization"
    - "revisiting singing voice detection a quantitative review and the future outlook"

Connected through hyperedge HE-6 (induced by source paper "towards interpretable polyphonic transcription with invertible neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "model agnostic supervised local explanations"
    - "excessive invariance causes adversarial vulnerability"
    - "i revnet deep invertible networks"
    - "generative modeling using the sliced wasserstein distance"
    - "an experimental analysis of the entanglement problem in neural network based music transcription systems"
    - "progressive growing of gans for improved quality stability and variation"
    - "onsets and frames dual objective piano transcription"

Connected through hyperedge HE-7 (induced by source paper "deep learning based automatic downbeat tracking a brief review"):
  with members:
    - "a universal music translation network"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "robust downbeat tracking using an ensemble of convolutional networks"
    - "music transcription modelling and composition using deep learning"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "improvements to deep convolutional neural networks for lvcsr"

Connected through hyperedge HE-8 (induced by source paper "deep unsupervised drum transcription"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "towards multi instrument drum transcription"
    - "a neural parametric singing synthesizer"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "u net convolutional networks for biomedical image segmentation"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)