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 "harmonic sum based method for heart rate estimation using ppg signals affected with motion artifacts"):
  Abstract: Wearable photoplethysmography (WPPG) has recently become a common technology in heart rate (HR) monitoring. General observation is that the motion artifacts change the statistics of the acquired PPG signal. Consequently, estimation of HR from such a corrupted PPG signal is challenging. However, if an accelerometer is also used to acquire the acceleration signal simultaneously, it can provide helpful information that can be used to reduce the motion artifacts in the PPG signal. By dint of repetitive movements of the subjects hands while running, the accelerometer signal is found to be quasi-periodic. Over short-time intervals, it can be modeled by a finite harmonic sum (HSUM). Using the harmonic sum (HSUM) model, we obtain an estimate of the instantaneous fundamental frequency of the accelerometer signal. Since the PPG signal is a composite of the heart rate information (that is also quasi-periodic) and the motion artifact, we fit a joint harmonic sum (HSUM) model to the PPG signal. One of the harmonic sums corresponds to the heart-beat component in PPG and the other models the motion artifact. However, the fundamental frequency of the motion artifact has already been determined from the accelerometer signal. Subsequently, the HR is estimated from the joint HSUM model. The mean absolute error in HR estimates was 0.7359 beats per minute (BPM) with a standard deviation of 0.8328 BPM for 2015 IEEE Signal Processing (SP) cup data. The ground-truth HR was obtained from the simultaneously acquired ECG for validating the accuracy of the proposed method. The proposed method is compared with four methods that were recently developed and evaluated on the same dataset.

Includes members:
  Member "a robust diarization system for measuring dominance in peer led team learning groups":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "toeplitz inverse covariance based robust speaker clustering for naturalistic audio streams")
  Member "a speaker diarization system for studying peer led team learning groups":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "toeplitz inverse covariance based robust speaker clustering for naturalistic audio streams")
  Member "fit a fog computing device for speech teletreatments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey of fog computing and communication current researches and future directions")
  Member "echowear smartwatch technology for voice and speech treatments of patients with parkinson s disease":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey of fog computing and communication current researches and future directions")
  Member "a multi smartwatch system for assessing speech characteristics of people with dysarthria in group settings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "smartear smartwatch based unsupervised learning for multi modal signal analysis in opportunistic sensing framework")
  Member "photoplethysmography based heart rate monitoring in physical activities via joint sparse spectrum reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "heart rate tracking using wrist type photoplethysmographic ppg signals during physical exercise with simultaneous accelerometry")
  Member "troika a general framework for heart rate monitoring using wrist type photoplethysmographic signals during intensive physical exercise":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an affective situation labeling system from psychological behaviors in emotion recognition")
  Member "fog data enhancing telehealth big data through fog computing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on edge computing systems and tools")

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.CY(Computers and Society)