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 "deep learning models for denoising ecg signals"):
  Abstract: Effective and powerful methods for denoising electrocardiogram (ECG) signals are important for wearable sensors and devices. Deep Learning (DL) models have been used extensively in image processing and other domains with great successes but only very recently they have been used in processing ECG signals. This paper presents two DL models, together with a standard wavelet-based technique for denoising ECG signals. First, a Convolutional Neural Network (CNN) is depicted and applied to noisy ECG signals. It includes six convolutional layers, with subsequent pooling and a fully connected layer for regression. The second DL model is a Long Short-Term Memory (LSTM) model, consisting of two LSTM layers. A wavelet technique based on an empirical Bayesian method with a Cauchy prior is also applied for comparison with the DL models, which are trained and tested on two synthetic datasets and a dataset containing real ECG signals. The results demonstrate that while both DL models were capable of dealing with heavy and drifting noise, the CNN model was markedly superior to the LSTM model in terms of the Root Mean Squared (RMS) error, and the wavelet technique was suitable only for rejecting random noise.

Includes members:
  Member "deep recurrent neural networks for ecg signal denoising":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on regularization properties of artificial datasets for deep learning")
  Member "cardiologist level arrhythmia detection with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "model assertions for monitoring and improving ml model")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "a neural network approach to ecg denoising":
    also connected through other hyperedges:
      (none)

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.LG(Machine Learning)