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: "deep neural networks for single channel source separation"
  Abstract: In this paper, a novel approach for single channel source separation (SCSS) using a deep neural network (DNN) architecture is introduced. Unlike previous studies in which DNN and other classifiers were used for classifying time-frequency bins to obtain hard masks for each source, we use the DNN to classify estimated source spectra to check for their validity during separation. In the training stage, the training data for the source signals are used to train a DNN. In the separation stage, the trained DNN is utilized to aid in estimation of each source in the mixed signal. Single channel source separation problem is formulated as an energy minimization problem where each source spectra estimate is encouraged to fit the trained DNN model and the mixed signal spectrum is encouraged to be written as a weighted sum of the estimated source spectra. The proposed approach works regardless of the energy scale differences between the source signals in the training and separation stages. Nonnegative matrix factorization (NMF) is used to initialize the DNN estimate for each source. The experimental results show that using DNN initialized by NMF for source separation improves the quality of the separated signal compared with using NMF for source separation.

Connected through hyperedge HE-1 (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"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "wildmix dataset and spectro temporal transformer model for monoaural audio source separation"):
  with members:
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "joint optimization of masks and deep recurrent neural networks for monaural source separation"
    - "deep transform cocktail party source separation via probabilistic re synthesis"
    - "adam a method for stochastic optimization"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-3 (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"
    - "multi resolution fully convolutional neural networks for monaural audio source separation"
    - "deep karaoke extracting vocals from musical mixtures using a convolutional deep neural network"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "learning latent representations for speech generation and transformation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "end to end source separation with adaptive front ends"):
  with members:
    - "single channel audio source separation using convolutional denoising autoencoders"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-6 (induced by source paper "discriminative enhancement for single channel audio source separation using deep neural networks"):
  with members:
    - "joint optimization of masks and deep recurrent neural networks for monaural source separation"
    - "deep karaoke extracting vocals from musical mixtures using a convolutional deep neural network"
    - "theano new features and speed improvements"

Connected through hyperedge HE-7 (induced by source paper "deep karaoke extracting vocals from musical mixtures using a convolutional deep neural network"):
  with members:
    - "probabilistic binary mask cocktail party source separation in a convolutional deep neural network"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-8 (induced by source paper "joint optimization of masks and deep recurrent neural networks for monaural source separation"):
  with members:
    - "how to construct deep recurrent neural networks"

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.NE(Neural and Evolutionary Computing)