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: "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
  Abstract: Deep neural networks have become an indispensable technique for audio source separation (ASS). It was recently reported that a variant of CNN architecture called MMDenseNet was successfully employed to solve the ASS problem of estimating source amplitudes, and state-of-the-art results were obtained for DSD100 dataset. To further enhance MMDenseNet, here we propose a novel architecture that integrates long short-term memory (LSTM) in multiple scales with skip connections to efficiently model long-term structures within an audio context. The experimental results show that the proposed method outperforms MMDenseNet, LSTM and a blend of the two networks. The number of parameters and processing time of the proposed model are significantly less than those for simple blending. Furthermore, the proposed method yields better results than those obtained using ideal binary masks for a singing voice separation task.

Connected through hyperedge HE-1 (induced by source paper "time domain audio source separation based on wave u net combined with discrete wavelet transform"):
  with members:
    - "making convolutional networks shift invariant again"
    - "universal sound separation"
    - "end to end music source separation is it possible in the waveform domain"
    - "end to end sound source separation conditioned on instrument labels"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "end to end source separation with adaptive front ends"
    - "multi scale multi band densenets for audio source separation"
    - "density estimation using real nvp"

Connected through hyperedge HE-2 (induced by source paper "meta learning extractors for music source separation"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "audio query based music source separation"
    - "on the variance of the adaptive learning rate and beyond"
    - "recursive speech separation for unknown number of speakers"
    - "lookahead optimizer k steps forward 1 step back"
    - "meta learning a dynamical language model"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "contextual parameter generation for universal neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "a dual staged context aggregation method towards efficient end to end speech enhancement"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "incorporating symbolic sequential modeling for speech enhancement"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "segan speech enhancement generative adversarial network"
    - "wavenet a generative model for raw audio"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "demucs deep extractor for music sources with extra unlabeled data remixed"):
  with members:
    - "sing symbol to instrument neural generator"
    - "end to end music source separation is it possible in the waveform domain"
    - "kymatio scattering transforms in python"
    - "a style based generator architecture for generative adversarial networks"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "denoising auto encoder with recurrent skip connections and residual regression for music source separation"
    - "progressive growing of gans for improved quality stability and variation"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

Connected through hyperedge HE-5 (induced by source paper "finding strength in weakness learning to separate sounds with weak supervision"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "audio source separation using variational autoencoders and weak class supervision"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "cutting music source separation some slakh a dataset to study the impact of training data quality and quantity"
    - "universal sound separation"
    - "co separating sounds of visual objects"
    - "sdr half baked or well done"

Connected through hyperedge HE-6 (induced by source paper "music source separation in the waveform domain"):
  with members:
    - "unsupervised singing voice conversion"
    - "fixup initialization residual learning without normalization"
    - "sing symbol to instrument neural generator"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "end to end music source separation is it possible in the waveform domain"
    - "a style based generator architecture for generative adversarial networks"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "denoising auto encoder with recurrent skip connections and residual regression for music source separation"

Connected through hyperedge HE-7 (induced by source paper "dilated convolution with dilated gru for music source separation"):
  with members:
    - "an overview of lead and accompaniment separation in music"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "denoising auto encoder with recurrent skip connections and residual regression for music source separation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "feudal networks for hierarchical reinforcement learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "multi scale multi band densenets for audio source separation"
    - "dilated recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "universal sound separation"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "fixup initialization residual learning without normalization"
    - "sdr half baked or well done"
    - "speaker independent speech separation with deep attractor network"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "differentiable consistency constraints for improved deep speech enhancement"
    - "exploring tradeoffs in models for low latency speech enhancement"
    - "a joint separation classification model for sound event detection of weakly labelled data"

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)