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: "source separation using regularized nmf with mmse estimates under gmm priors with online learning for the uncertainties"
  Abstract: We propose a new method to enforce priors on the solution of the nonnegative matrix factorization (NMF). The proposed algorithm can be used for denoising or single-channel source separation (SCSS) applications. The NMF solution is guided to follow the Minimum Mean Square Error (MMSE) estimates under Gaussian mixture prior models (GMM) for the source signal. In SCSS applications, the spectra of the observed mixed signal are decomposed as a weighted linear combination of trained basis vectors for each source using NMF. In this work, the NMF decomposition weight matrices are treated as a distorted image by a distortion operator, which is learned directly from the observed signals. The MMSE estimate of the weights matrix under GMM prior and log-normal distribution for the distortion is then found to improve the NMF decomposition results. The MMSE estimate is embedded within the optimization objective to form a novel regularized NMF cost function. The corresponding update rules for the new objectives are derived in this paper. Experimental results show that, the proposed regularized NMF algorithm improves the source separation performance compared with using NMF without prior or with other prior models.

Connected through hyperedge HE-1 (induced by source paper "single channel audio source separation using convolutional denoising autoencoders"):
  with members:
    - "convolutional gated recurrent neural network incorporating spatial features for audio tagging"
    - "deep convolutional neural networks for predominant instrument recognition in polyphonic music"
    - "a fully convolutional neural network for speech enhancement"
    - "a fully convolutional deep auditory model for musical chord recognition"
    - "a neural network alternative to non negative audio models"
    - "discriminative enhancement for single channel audio source separation using deep neural networks"
    - "convolutional recurrent neural networks for music classification"
    - "theano new features and speed improvements"

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"
    - "time frequency trade offs for audio source separation with binary masks"
    - "adam a method for stochastic optimization"

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.LG(Machine Learning)