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: "convolutional recurrent neural networks for speech enhancement"
  Abstract: We propose an end-to-end model based on convolutional and recurrent neural networks for speech enhancement. Our model is purely data-driven and does not make any assumptions about the type or the stationarity of the noise. In contrast to existing methods that use multilayer perceptrons (MLPs), we employ both convolutional and recurrent neural network architectures. Thus, our approach allows us to exploit local structures in both the frequency and temporal domains. By incorporating prior knowledge of speech signals into the design of model structures, we build a model that is more data-efficient and achieves better generalization on both seen and unseen noise. Based on experiments with synthetic data, we demonstrate that our model outperforms existing methods, improving PESQ by up to 0.6 on seen noise and 0.64 on unseen noise.

Connected through hyperedge HE-1 (induced by source paper "deep speech enhancement for reverberated and noisy signals using wide residual networks"):
  with members:
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "segan speech enhancement generative adversarial network"
    - "decoupled weight decay regularization"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "a fully convolutional neural network for speech enhancement"
    - "wide residual networks"
    - "musan a music speech and noise corpus"

Connected through hyperedge HE-2 (induced by source paper "convolutional recurrent neural network based progressive learning for monaural speech enhancement"):
  with members:
    - "progressive image deraining networks a better and simpler baseline"
    - "constrained convolutional recurrent networks to improve speech quality with low impact on recognition accuracy"
    - "supervised speech separation based on deep learning an overview"
    - "a fully convolutional neural network for speech enhancement"
    - "pruning filters for efficient convnets"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deeply recursive convolutional network for image super resolution"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"):
  with members:
    - "multi scale multi band densenets for audio source separation"
    - "aenet learning deep audio features for video analysis"
    - "deep convolutional neural networks and data augmentation for acoustic event detection"
    - "a fully convolutional deep auditory model for musical chord recognition"
    - "wide residual networks"
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-4 (induced by source paper "using recurrences in time and frequency within u net architecture for speech enhancement"):
  with members:
    - "deep learning for environmentally robust speech recognition an overview of recent developments"
    - "supervised speech separation based on deep learning an overview"
    - "single channel audio source separation using convolutional denoising autoencoders"
    - "a fully convolutional neural network for speech enhancement"
    - "joint optimization of masks and deep recurrent neural networks for monaural source separation"
    - "u net convolutional networks for biomedical image segmentation"
    - "renet a recurrent neural network based alternative to convolutional 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.SD(Sound)