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 recurrent neural networks for ecg signal denoising"
  Abstract: We present a novel approach to denoise electrocardiographic signals (ECG), utilizing deep recurrent neural network built of Long-Short Term Memory (LSTM) units. The network is pretrained using synthetic data, generated by dynamic model ECG and fine-tuned with a real data from Physionet PDB database of ECG signals. The results show that a 10-layer DRNN has a mean squared error as low as 0.179 for denoising real signals with white noise of amplitude 0.2 mV, making it a viable alternative for other commonly used methods. We also investigate the impact of synthetic data on the network performance on real signals. Our results show that networks pretrained with synthetic data have better results than network trained with real data only, regardless of the training set size. We propose to explain this by means of the transfer learning framework and the analogy to human cognitive process.

Connected through hyperedge HE-1 (induced by source paper "deep learning models for denoising ecg signals"):
  with members:
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "deep residual learning for image recognition"
    - "a neural network approach to ecg denoising"

Connected through hyperedge HE-2 (induced by source paper "on regularization properties of artificial datasets for deep learning"):
  with members:
    - "when does label smoothing help"
    - "understanding deep learning requires rethinking generalization"

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)