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: "comparative analysis of spatialhadoop and geospark for geospatial big data analytics"
  Abstract: In this digitalised world where every information is stored, the data a are growing exponentially. It is estimated that data are doubles itself every two years. Geospatial data are one of the prime contributors to the big data scenario. There are numerous tools of the big data analytics. But not all the big data analytics tools are capabilities to handle geospatial big data. In the present paper, it has been discussed about the recent two popular open source geospatial big data analytical tools i.e. Spatial- Hadoop and GeoSpark which can be used for analysis and process the geospatial big data in efficient manner. It has compared the architectural view of SpatialHadoop and GeoSpark. Through the architectural comparison, it has also summarised the merits and demerits of these tools according the execution times and volume of the data which has been used.

Connected through hyperedge HE-1 (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"
    - "fog computing in medical internet of things architecture implementation and applications"
    - "foggis fog computing 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-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"
    - "smart fog fog computing framework for unsupervised clustering analytics in wearable internet of things"
    - "fog computing in medical internet of things architecture implementation and applications"
    - "foggis fog computing for geospatial big data analytics"
    - "fog data enhancing telehealth big data through fog computing"

Connected through hyperedge HE-3 (induced by source paper "foglearn leveraging fog based machine learning for smart system big data analytics"):
  with members:
    - "fog computing in medical internet of things architecture implementation and applications"
    - "foggis fog computing for geospatial big data analytics"
    - "fog computing principles architectures and applications"
    - "fog data enhancing telehealth big data through fog computing"

Connected through hyperedge HE-4 (induced by source paper "fair a hadoop based hybrid model for faculty information retrieval system"):
  with members:
    - "foggis fog computing for geospatial big data analytics"
    - "fit a fog computing device for speech teletreatments"
    - "mllib machine learning in apache spark"
    - "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)