human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "convolutional recurrent neural network based progressive learning for monaural speech enhancement"):
  Abstract: Recently, progressive learning has shown its capacity of improving speech quality and speech intelligibility when it is combined with deep neural network (DNN) and long short-term memory (LSTM) based monaural speech enhancement algorithms, especially in low signal-to-noise ratio (SNR) conditions. Nevertheless, due to a large number of parameters and highly computational complexity, it is hard to implement in current resource-limited micro-controllers and thus, it is important to significantly reduce both the amount of parameters and the computational load for practical applications. For this purpose, we propose a novel progressive learning framework with convolutional recurrent neural networks called PL-CRNN, which takes advantages of both convolutional neural networks and recurrent neural networks to drastically reduce the amount of parameters and simultaneously improve speech quality and speech intelligibility. Numerous experiments verify the effectiveness of proposed PL-CRNN model and indicate that it yields consistent better performance than the PL-DNN and PL-LSTM algorithms and also it gets results close even better than the CRNN in terms of various evaluation metrics. Compared with PL-DNN, PL-LSTM and state-of-the-art CRNN models, the proposed PL-CRNN algorithm can reduce the amount of parameters up to 77\%, 93\% and 93\%, respectively.

Includes members:
  Member "progressive image deraining networks a better and simpler baseline":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")
  Member "constrained convolutional recurrent networks to improve speech quality with low impact on recognition accuracy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "direct optimization of f measure for retrieval based personal question answering")
  Member "convolutional recurrent neural networks for speech enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep speech enhancement for reverberated and noisy signals using wide residual networks")
  Member "supervised speech separation based on deep learning an overview":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech separation")
  Member "a fully convolutional neural network for speech enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement")
  Member "pruning filters for efficient convnets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "deeply recursive convolutional network for image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning enriched features for real image restoration and enhancement")

Question: Please tell me which class the source paper 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)