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: "multi resolution fully convolutional neural networks for monaural audio source separation"
  Abstract: In deep neural networks with convolutional layers, each layer typically has fixed-size/single-resolution receptive field (RF). Convolutional layers with a large RF capture global information from the input features, while layers with small RF size capture local details with high resolution from the input features. In this work, we introduce novel deep multi-resolution fully convolutional neural networks (MR-FCNN), where each layer has different RF sizes to extract multi-resolution features that capture the global and local details information from its input features. The proposed MR-FCNN is applied to separate a target audio source from a mixture of many audio sources. Experimental results show that using MR-FCNN improves the performance compared to feedforward deep neural networks (DNNs) and single resolution deep fully convolutional neural networks (FCNNs) on the audio source separation problem.

Connected through hyperedge HE-1 (induced by source paper "multichannel speech enhancement by raw waveform mapping using fully convolutional networks"):
  with members:
    - "dilated fcn listening longer to hear better"
    - "adversarial audio synthesis"
    - "speech dereverberation based on integrated deep and ensemble learning algorithm"
    - "raw multi channel audio source separation using multi resolution convolutional auto encoders"
    - "segan speech enhancement generative adversarial network"
    - "a wavenet for speech denoising"
    - "wavenet a generative model for raw audio"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-2 (induced by source paper "multi band multi resolution fully convolutional neural networks for singing voice separation"):
  with members:
    - "multi scale multi band densenets for audio source separation"
    - "deep karaoke extracting vocals from musical mixtures using a convolutional deep neural network"
    - "adam a method for stochastic optimization"
    - "deep neural networks for single channel source separation"

Connected through hyperedge HE-3 (induced by source paper "multi resolution networks for semantic segmentation in whole slide images"):
  with members:
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "a deep learning framework for automatic diagnosis in lung cancer"
    - "dilated residual networks"
    - "learning to segment breast biopsy whole slide images"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "music source separation using stacked hourglass networks"):
  with members:
    - "multi scale multi band densenets for audio source separation"
    - "a recurrent encoder decoder approach with skip filtering connections for monaural singing voice separation"
    - "stacked hourglass networks for human pose estimation"
    - "associative embedding end to end learning for joint detection and grouping"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "deep karaoke extracting vocals from musical mixtures using a convolutional deep neural network"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "multilevel artificial neural network training for spatially correlated learning"):
  with members:
    - "parallelizing over artificial neural network training runs with multigrid"
    - "approximating the spectrum of a graph"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "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.SD(Sound)