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 "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.

Includes members:
  Member "fog assisted wiot a smart fog gateway for end to end analytics in wearable internet of things":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "healthfog an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments")
  Member "fog computing in medical internet of things architecture implementation and applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "embedded deep learning in ophthalmology making ophthalmic imaging smarter")
  Member "bigear inferring the ambient and emotional correlates from smartphone based acoustic big data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "smartear smartwatch based unsupervised learning for multi modal signal analysis in opportunistic sensing framework")
  Member "harmonic sum based method for heart rate estimation using ppg signals affected with motion artifacts":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fog computing in medical internet of things architecture implementation and applications")
  Member "fit a fog computing device for speech teletreatments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (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-7 (induced by source paper "smartear smartwatch based unsupervised learning for multi modal signal analysis in opportunistic sensing framework")
  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)