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: "a hybrid approach for data analytics for internet of things"
  Abstract: The vision of the Internet of Things is to allow currently unconnected physical objects to be connected to the internet. There will be an extremely large number of internet connected devices that will be much more than the number of human being in the world all producing data. These data will be collected and delivered to the cloud for processing, especially with a view of finding meaningful information to then take action. However, ideally the data needs to be analysed locally to increase privacy, give quick responses to people and to reduce use of network and storage resources. To tackle these problems, distributed data analytics can be proposed to collect and analyse the data either in the edge or fog devices. In this paper, we explore a hybrid approach which means that both innetwork level and cloud level processing should work together to build effective IoT data analytics in order to overcome their respective weaknesses and use their specific strengths. Specifically, we collected raw data locally and extracted features by applying data fusion techniques on the data on resource constrained devices to reduce the data and then send the extracted features to the cloud for processing. We evaluated the accuracy and data consumption over network and thus show that it is feasible to increase privacy and maintain accuracy while reducing data communication demands.

Connected through hyperedge HE-1 (induced by source paper "a pub sub sdn integrated framework for iot traffic orchestration"):
  with members:
    - "a publish subscribe qos aware framework for massive iot traffic orchestration"
    - "g bam a generalized bandwidth allocation model for ip mpls ds te networks"
    - "evolving sdn for low power iot networks"
    - "foggy a platform for workload orchestration in a fog computing environment"
    - "city data fusion sensor data fusion in the internet of things"
    - "software defined networking a comprehensive survey"
    - "alloctc sharing a new bandwidth allocation model for ds te networks"

Connected through hyperedge HE-2 (induced by source paper "exploring the effectiveness of service decomposition in fog computing architecture for the internet of things"):
  with members:
    - "fog computing for sustainable smart cities a survey"
    - "enorm a framework for edge node resource management"
    - "microservices granularity vs performance"
    - "privacy preserving personal model training"
    - "valorising the iot databox creating value for everyone"
    - "a survey on internet of things from industrial market perspective"

Connected through hyperedge HE-3 (induced by source paper "a spatial temporal correlation approach for data reduction in cluster based sensor networks"):
  with members:
    - "energy efficient location and activity aware on demand mobile distributed sensing platform for sensing as a service in iot clouds"
    - "efficient opportunistic sensing using mobile collaborative platform mosden"

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.NI(Networking and Internet Architecture)