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 "fog computing in medical internet of things architecture implementation and applications"):
  Abstract: In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one’s health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: (1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex; (2) The data, when communicated, are vulnerable to security and privacy issues; (3) The communication of the continuously collected data is not only costly but also energy hungry; (4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection. The book chapter ends with experiments and results showing how fog computing could lessen the obstacles of existing cloud-driven medical IoT solutions and enhance the overall performance of the system in terms of computing intelligence, transmission, storage, configurable, and security. The case studies on various types of physiological data shows that the proposed Fog architecture could be used for signal enhancement, processing and analysis of various types of bio-signals.

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 "bigear inferring the ambient and emotional correlates from smartphone based acoustic big data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "smart fog fog computing framework for unsupervised clustering analytics in wearable internet of things")
  Member "foggis fog computing for geospatial big data analytics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "challenges in designing edge based middlewares for the internet of things a survey")
  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.DC(Distributed, Parallel, and Cluster Computing)