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: "discriminative enhancement for single channel audio source separation using deep neural networks"
  Abstract: The sources separated by most single channel audio source separation techniques are usually distorted and each separated source contains residual signals from the other sources. To tackle this problem, we propose to enhance the separated sources to decrease the distortion and interference between the separated sources using deep neural networks (DNNs). Two different DNNs are used in this work. The first DNN is used to separate the sources from the mixed signal. The second DNN is used to enhance the separated signals. To consider the interactions between the separated sources, we propose to use a single DNN to enhance all the separated sources together. To reduce the residual signals of one source from the other separated sources (interference), we train the DNN for enhancement discriminatively to maximize the dissimilarity between the predicted sources. The experimental results show that using discriminative enhancement decreases the distortion and interference between the separated sources.

Connected through hyperedge HE-1 (induced by source paper "an overview of lead and accompaniment separation in music"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "multi scale multi band densenets for audio source separation"
    - "a recurrent encoder decoder approach with skip filtering connections for monaural singing voice separation"
    - "monaural singing voice separation with skip filtering connections and recurrent inference of time frequency mask"
    - "single channel multi speaker separation using deep clustering"
    - "singing voice separation and vocal f0 estimation based on mutual combination of robust principal component analysis and subharmonic summation"
    - "joint optimization of masks and deep recurrent neural networks for monaural source separation"
    - "deep clustering discriminative embeddings for segmentation and separation"

Connected through hyperedge HE-2 (induced by source paper "non intrusive load monitoring with fully convolutional networks"):
  with members:
    - "wavenet a generative model for raw audio"
    - "neural nilm deep neural networks applied to energy disaggregation"
    - "multi scale context aggregation by dilated convolutions"
    - "adam a method for stochastic optimization"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-3 (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"
    - "convolutional recurrent neural networks for music classification"
    - "source separation using regularized nmf with mmse estimates under gmm priors with online learning for the uncertainties"
    - "theano new features and speed improvements"

Connected through hyperedge HE-4 (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"
    - "time frequency trade offs for audio source separation with binary masks"
    - "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)