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 novel deep transfer learning method for detection of myocardial infarction"
  Abstract: Myocardial infarction (MI), also known as a cardiac attack, is one of the common cardiac disorders occurs when one or more coronary arteries are blocked. Hence, early detection of MI is critical for the reduction of the rising of the death rate. The cardiologists use the electrocardiogram (ECG) as a diagnostic tool to monitor and reveal the MI signals. However, all the MI signals are not constant and noisy, so it is tough to detect or observe these signals manually. Several computer-aided diagnosis systems (CADs) have been suggested to solve these difficulties. In this paper, we have proposed an effective CAD system to detect MI signals using the two-dimensional convolution neural network (CNN). In this study, we have employed two ways of the transfer learning technique to retrain the pre-trained VGG-Net and obtained two new networks VGG-MI1 and VGG-MI2. Moreover, the heartbeat data augmentation techniques are employed to increase the classification performance. We have utilized two-second ECG signals from the PTB database, which has been widely employed in MI detection studies. In case of using VGG-MI1, we achieved an accuracy, sensitivity, and specificity of 99.02%, 98.76%, and 99.17% respectively and we achieved an accuracy of 99.22%, a sensitivity of 99.15%, and a specificity of 99.49% when using VGG-MI2. Results showed that the proposed algorithm is more efficient than the state-of-the-art methods in terms of accuracy sensitivity, and specificity. Finally, the proposed algorithm can assist the specialists to detect the MI signals more precisely.

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.LG(Machine Learning)