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: "smart fog fog computing framework for unsupervised clustering analytics in wearable internet of things"
  Abstract: The increasing use of wearables in smart telehealth generates heterogeneous medical big data. Cloud and fog services process these data for assisting clinical procedures. IoT based ehealthcare have greatly benefited from efficient data processing. This paper proposed and evaluated use of low resource machine learning on Fog devices kept close to the wearables for smart healthcare. In state of the art telecare systems, the signal processing and machine learning modules are deployed in the cloud for processing physiological data. We developed a prototype of Fog-based unsupervised machine learning big data analysis for discovering patterns in physiological data. We employed Intel Edison and Raspberry Pi as Fog computer in proposed architecture. We performed validation studies on real-world pathological speech data from in home monitoring of patients with Parkinson's disease (PD). Proposed architecture employed machine learning for analysis of pathological speech data obtained from smartwatches worn by the patients with PD. Results showed that proposed architecture is promising for low-resource clinical machine learning. It could be useful for other applications within wearable IoT for smart telehealth scenarios by translating machine learning approaches from the cloud backend to edge computing devices such as Fog.

Connected through hyperedge HE-1 (induced by source paper "smartear smartwatch based unsupervised learning for multi modal signal analysis in opportunistic sensing framework"):
  with members:
    - "fog assisted wiot a smart fog gateway for end to end analytics in wearable internet of things"
    - "soa fog secure service oriented edge computing architecture for smart health big data analytics"
    - "fog computing in medical internet of things architecture implementation and applications"
    - "bigear inferring the ambient and emotional correlates from smartphone based acoustic big data"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "soa fog secure service oriented edge computing architecture for smart health big data analytics"):
  with members:
    - "fog assisted wiot a smart fog gateway for end to end analytics in wearable internet of things"
    - "fog computing for sustainable smart cities a survey"
    - "fog computing in medical internet of things architecture implementation and applications"
    - "foggis fog computing for geospatial big data analytics"
    - "comparative analysis of spatialhadoop and geospark for geospatial big data analytics"
    - "fog data enhancing telehealth big data through fog computing"

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)