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: "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.

Connected through hyperedge HE-1 (induced by source paper "embedded deep learning in ophthalmology making ophthalmic imaging smarter"):
  with members:
    - "all one needs to know about fog computing and related edge computing paradigms a complete survey"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "the intelligent icu pilot study using artificial intelligence technology for autonomous patient monitoring"
    - "towards safe deep learning accurately quantifying biomarker uncertainty in neural network predictions"
    - "towards efficient convolutional neural network for domain specific applications on fpga"
    - "an unsupervised learning model for deformable medical image registration"
    - "a deep learning approach to denoise optical coherence tomography images of the optic nerve head"

Connected through hyperedge HE-2 (induced by source paper "a view on edge caching applications"):
  with members:
    - "the rich prefetching in edge caches for in order delivery to connected cars"
    - "smart edge cocaco ai enabled smart edge with joint computation caching and communication in heterogeneous iot"
    - "communications caching and computing for mobile virtual reality modeling and tradeoff"
    - "air ground integrated vehicular network slicing with content pushing and caching"
    - "caching aided collaborative d2d operation for predictive data dissemination in industrial iot"
    - "a survey on mobile edge networks convergence of computing caching and communications"

Connected through hyperedge HE-3 (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"
    - "smart fog fog computing framework for unsupervised clustering analytics in wearable internet of things"
    - "soa fog secure service oriented edge computing architecture for smart health big data analytics"
    - "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-4 (induced by source paper "fogbus a blockchain based lightweight framework for edge and fog computing"):
  with members:
    - "fog computing a taxonomy survey and future directions"
    - "internet of things iot and new computing paradigms"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-5 (induced by source paper "smart fog fog computing framework for unsupervised clustering analytics in wearable internet of things"):
  with members:
    - "fog assisted wiot a smart fog gateway for end to end analytics in wearable internet of things"
    - "bigear inferring the ambient and emotional correlates from smartphone based acoustic big data"
    - "harmonic sum based method for heart rate estimation using ppg signals affected with motion artifacts"
    - "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"
    - "fog data enhancing telehealth big data through fog computing"

Connected through hyperedge HE-6 (induced by source paper "fog computing based sdi framework for mineral resources information infrastructure management in india"):
  with members:
    - "fog assisted wiot a smart fog gateway for end to end analytics in wearable internet of things"
    - "foggis fog computing for geospatial big data analytics"
    - "comparative analysis of spatialhadoop and geospark for geospatial big data analytics"
    - "fit a fog computing device for speech teletreatments"
    - "fog computing principles architectures and applications"
    - "echowear smartwatch technology for voice and speech treatments of patients with parkinson s disease"
    - "fog data enhancing telehealth big data through fog computing"

Connected through hyperedge HE-7 (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"
    - "smart fog fog computing framework for unsupervised clustering analytics in wearable internet of things"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "foglearn leveraging fog based machine learning for smart system big data analytics"):
  with members:
    - "foggis fog computing for geospatial big data analytics"
    - "comparative analysis of spatialhadoop and geospark for geospatial big data analytics"
    - "fog computing principles architectures and applications"
    - "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.DC(Distributed, Parallel, and Cluster Computing)