human: Given a node-centered hypergraph: <hypergraph>, where nodes represent authors and hyperedges represent groups of authors who co-authored the same paper.

Details of the hypergraph:
Center node: "Automatic identification of cardiac health using modeling techniques: A comparative study"
  Abstract: Heart rate variability (HRV), a widely adopted quantitative marker of the autonomic nervous system can be used as a predictor of risk of cardiovascular diseases. Moreover, decreased heart rate variability (HRV) has been associated with an increased risk of cardiovascular diseases. Hence in this work HRV signal is used as the base signal for predicting the risk of cardiovascular diseases. The present study concerns nine cardiac classes that include normal sinus rhythm (NSR), congestive heart failure (CHF), atrial fibrillation (AF), ventricular fibrillation (VF), preventricular contraction (PVC), left bundle branch block (LBBB), complete heart block (CHB), ischemic/dilated cardiomyopathy (ISCH) and sick sinus syndrome (SSS). A total of 352 cardiac subjects belonging to the nine classes were analyzed in the frequency domain. The fast Fourier transforms (FFT) and three other modeling techniques namely, autoregressive (AR) model, moving average (MA) model and the autoregressive moving average (ARMA) model are used to estimate the power spectral densities of the RR interval variability. The spectral parameters obtained from the spectral analysis of the HRV signals are used as the input parameters to the artificial neural network (ANN) for classification of the different cardiac classes. Our findings reveal that the ARMA modeling technique seems to give better resolution and would be more promising for clinical diagnosis.

Connected through hyperedge HE-1 (induced by source paper "Sudden cardiac death (SCD) prediction based on nonlinear heart rate variability features and SCD index"):
  with members:
    - "Coronary artery disease detection using computational intelligence methods"

Connected through hyperedge HE-2 (induced by source paper "Performance study of Byzantine Agreement Protocol with artificial neural network"):
  with members:
    - "Practical Byzantine fault tolerance"

Question: Please tell me which research area the center author belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Mathematics