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: "efficient opportunistic sensing using mobile collaborative platform mosden"
  Abstract: Mobile devices are rapidly becoming the primary computing device in people's lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of these applications take advantage of the plethora of sensors installed on the mobile device to deliver enhanced user experience. The sensors on the smartphone provide the opportunity to develop innovative mobile opportunistic sensing applications in many sectors including healthcare, environmental monitoring and transportation. In this paper, we present a collaborative mobile sensing framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on smartphones capturing and sharing sensed data between multiple distributed applications and users. MOSDEN follows a component-based design philosophy promoting reuse for easy and quick opportunistic sensing application deployments. MOSDEN separates the application-specific processing from the sensing, storing and sharing. MOSDEN is scalable and requires minimal development effort from the application developer. We have implemented our framework on Android-based mobile platforms and evaluate its performance to validate the feasibility and efficiency of MOSDEN to operate collaboratively in mobile opportunistic sensing applications. Experimental outcomes and lessons learnt conclude the paper.

Connected through hyperedge HE-1 (induced by source paper "maximizing clearance rate by penalizing redundant task assignment in mobile crowdsensing auctions"):
  with members:
    - "maximizing clearance rate of reputation aware auctions in mobile crowdsensing"
    - "distributed time sensitive task selection in mobile crowdsensing"

Connected through hyperedge HE-2 (induced by source paper "a spatial temporal correlation approach for data reduction in cluster based sensor networks"):
  with members:
    - "a hybrid approach for data analytics for internet of things"
    - "energy efficient location and activity aware on demand mobile distributed sensing platform for sensing as a service in iot clouds"

Connected through hyperedge HE-3 (induced by source paper "energy efficient location and activity aware on demand mobile distributed sensing platform for sensing as a service in iot clouds"):
  with members:
    - "the emerging internet of things marketplace from an industrial perspective a survey"
    - "a survey on internet of things from industrial market perspective"
    - "context aware dynamic discovery and configuration of things in smart environments"
    - "semantic driven configuration of internet of things middleware"
    - "mosden an internet of things middleware for resource constrained mobile devices"
    - "sensing as a service and big data"
    - "sensing as a service model for smart cities supported by internet of things"
    - "capturing sensor data from mobile phones using global sensor network middleware"

Connected through hyperedge HE-4 (induced by source paper "mosden a scalable mobile collaborative platform for opportunistic sensing applications"):
  with members:
    - "context aware computing for the internet of things a survey"
    - "sensing as a service and big data"
    - "cdas a crowdsourcing data analytics system"

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)