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: "multi parametric statistical method for estimation of accumulated fatigue by sensors in ordinary gadgets"
  Abstract: The new method is proposed to monitor the level of currently accumulated fatigue and estimate it by the several statistical methods. The experimental software application was developed and used to get data from sensors (accelerometer, GPS, gyroscope, magnetometer, and camera), conducted experiments, collected data, calculated parameters of their distributions (mean, standard deviation, skewness, kurtosis), and analyzed them by statistical methods (moment analysis, cluster analysis, bootstrapping, periodogram and spectrogram analyses). The hypothesis 1 (physical activity can be estimated and classified by moment and cluster analysis) and hypothesis 2 (fatigue can be estimated by moment analysis, bootstrapping analysis, periodogram, and spectrogram) were proposed and proved. Several "fatigue metrics" were proposed: location, size, shape of clouds of points on bootstrapping plot. The most promising fatigue metrics is the distance from the "rest" state point to the "fatigue" state point (sum of 3 squared non-normal distribution of non-correlated acceleration values) on the skewness-kurtosis plot. These hypotheses were verified on several persons of various age, gender, fitness level and improved standard statistical methods in similar researches. The method can be used in practice for ordinary people in everyday situations (to estimate their fatigue, give tips about it and advice on context-related information).

Connected through hyperedge HE-1 (induced by source paper "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities"):
  with members:
    - "deep learning for fatigue estimation on the basis of multimodal human machine interactions"
    - "augmented coaching ecosystem for non obtrusive adaptive personalized elderly care on the basis of cloud fog dew computing paradigm"
    - "automatized generation of alphabets of symbols"
    - "generating and estimating nonverbal alphabets for situated and multimodal communications"
    - "performance analysis of open source machine learning frameworks for various parameters in single threaded and multi threaded modes"
    - "synergy of volunteer measurements and volunteer computing for effective data collecting processing simulating and analyzing on a worldwide scale"

Connected through hyperedge HE-2 (induced by source paper "augmented coaching ecosystem for non obtrusive adaptive personalized elderly care on the basis of cloud fog dew computing paradigm"):
  with members:
    - "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities"
    - "synergy of volunteer measurements and volunteer computing for effective data collecting processing simulating and analyzing on a worldwide scale"
    - "science gateway for distributed multiscale course management in e science and e learning use case for study and investigation of functionalized nanomaterials"

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)