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: "time frequency trade offs for audio source separation with binary masks"
  Abstract: The short-time Fourier transform (STFT) provides the foundation of binary-mask based audio source separation approaches. In computing a spectrogram, the STFT window size parameterizes the trade-off between time and frequency resolution. However, it is not yet known how this parameter affects the operation of the binary mask in terms of separation quality for real-world signals such as speech or music. Here, we demonstrate that the trade-off between time and frequency in the STFT, used to perform ideal binary mask separation, depends upon the types of source that are to be separated. In particular, we demonstrate that different window sizes are optimal for separating different combinations of speech and musical signals. Our findings have broad implications for machine audition and machine learning in general.

Connected through hyperedge HE-1 (induced by source paper "raw multi channel audio source separation using multi resolution convolutional auto encoders"):
  with members:
    - "a tutorial on deep learning for music information retrieval"
    - "does phase matter for monaural source separation"
    - "single channel audio source separation using convolutional denoising autoencoders"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "multi resolution fully convolutional neural networks for monaural audio source separation"):
  with members:
    - "end to end source separation with adaptive front ends"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "a recurrent encoder decoder approach with skip filtering connections for monaural singing voice separation"
    - "single channel audio source separation using convolutional denoising autoencoders"
    - "a fully convolutional neural network for speech enhancement"
    - "discriminative enhancement for single channel audio source separation using deep neural networks"
    - "adam a method for stochastic optimization"
    - "source separation using regularized nmf with mmse estimates under gmm priors with online learning for the uncertainties"

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)