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: "a neural network approach to ecg denoising"
  Abstract: AbstractWe propose an ECG denoising method based on a feed forward neuralnetwork with three hidden layers. Particulary useful for very noisy signals,this approach uses the available ECG channels to reconstruct a noisychannel. We tested the method, on all the records from Physionet MIT-BIH Arrhythmia Database, adding electrode motion artifact noise. Thisdenoising method improved the perfomance of publicly available ECGanalysis programs on noisy ECG signals. This is an oine method thatcan be used to remove noise from very corrupted Holter records. 1 Introduction The ECG is often corrupted by di erent types of noise, namely, power line inter-ference, electrode contact and motion artifacts, respiration, electrical activityof muscles in the vicinity of the electrodes and interference from other elec-tronic devices. Analysis of noisy ECGs is dicult for humans and for computerprograms. In this work we place ourselves in context of automatic and semiautomatic ECG analysis: denoising should facilitate automatic ECG analysis.General denoising signal processing methods have been applied to ECG. Lowpass linear lters are used for high frequency noise removal, namely power lineinterference and muscle activity artifacts. High pass linear lters can be appliedto cancel baseline wander. The use of neural networks to ECG denoising hasbeen, to our knowledge, limited to the removal of these two types of noise. Otherdenoising tools are median lter, wavelet transform methods, empirical modedecomposition, morphological lters, non linear bayesian ltering and templatematching. We will focus on noise introduced by electrode motion which causesmore diculties in ECG analysis[16]. Our method adapts to each particularECG channel and learns how to reproduce it from a noisy version of the di erentchannels available.In the Physionet/Cinc Challenge 2010 it was shown that we can use somephysiological signals to reconstruct another physiological signal, in particular anECG [17, 20, 21]. This approach to reconstructing the noisy ECG channel is asimpli ed version, but equally e ective, of the winning entry in that Challenge.1

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

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.CE(Computational Engineering, Finance, and Science)