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: "troika a general framework for heart rate monitoring using wrist type photoplethysmographic signals during intensive physical exercise"
  Abstract: Heart rate monitoring using wrist-type photoplethysmographic signals during subjects’ intensive exercise is a difficult problem, since the signals are contaminated by extremely strong motion artifacts caused by subjects’ hand movements. So far few works have studied this problem. In this study, a general framework, termed TROIKA, is proposed, which consists of signal decomposiTion for denoising, sparse signal RecOnstructIon for high-resolution spectrum estimation, and spectral peaK trAcking with verification. The TROIKA framework has high estimation accuracy and is robust to strong motion artifacts. Many variants can be straightforwardly derived from this framework. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/h showed that the average absolute error of heart rate estimation was 2.34 beat per minute, and the Pearson correlation between the estimates and the ground truth of heart rate was 0.992. This framework is of great values to wearable devices such as smartwatches which use PPG signals to monitor heart rate for fitness.

Connected through hyperedge HE-1 (induced by source paper "an affective situation labeling system from psychological behaviors in emotion recognition"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"

Connected through hyperedge HE-2 (induced by source paper "wearable affective life log system for understanding emotion dynamics in daily life"):
  with members:
    - "information transfer in social media"

Connected through hyperedge HE-3 (induced by source paper "ppgnet deep network for device independent heart rate estimation from photoplethysmogram"):
  with members:
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"

Connected through hyperedge HE-4 (induced by source paper "harmonic sum based method for heart rate estimation using ppg signals affected with motion artifacts"):
  with members:
    - "a robust diarization system for measuring dominance in peer led team learning groups"
    - "a speaker diarization system for studying peer led team learning groups"
    - "fit a fog computing device for speech teletreatments"
    - "echowear smartwatch technology for voice and speech treatments of patients with parkinson s disease"
    - "a multi smartwatch system for assessing speech characteristics of people with dysarthria in group settings"
    - "photoplethysmography based heart rate monitoring in physical activities via joint sparse spectrum reconstruction"
    - "fog data enhancing telehealth big data through fog computing"

Connected through hyperedge HE-5 (induced by source paper "heart rate tracking using wrist type photoplethysmographic ppg signals during physical exercise with simultaneous accelerometry"):
  with members:
    - "photoplethysmography based heart rate monitoring in physical activities via joint sparse spectrum reconstruction"

Connected through hyperedge HE-6 (induced by source paper "bayesian de quantization and data compression for low energy physiological signal telemonitoring"):
  with members:
    - "low complexity multiclass encryption by compressed sensing"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "compressed sensing for energy efficient wireless telemonitoring challenges and opportunities"
    - "energy efficient telemonitoring of physiological signals via compressed sensing a fast algorithm and power consumption evaluation"
    - "model based compressive sensing"

Connected through hyperedge HE-7 (induced by source paper "photoplethysmography based heart rate monitoring in physical activities via joint sparse spectrum reconstruction"):
  with members:
    - "spatiotemporal sparse bayesian learning with applications to compressed sensing of multichannel physiological signals"
    - "joint sparsity recovery for spectral compressed sensing"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

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.CY(Computers and Society)