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: "vifi virtual fingerprinting wifi based indoor positioning via multi wall multi floor propagation model"
  Abstract: Widespread adoption of indoor positioning systems based on WiFi fingerprinting is at present hindered by the large efforts required for measurements collection during the offline phase. Two approaches were recently proposed to address such issue: crowdsourcing and RSS radiomap prediction based on either interpolation or propagation channel model fitting from a small set of measurements. RSS prediction promises better positioning accuracy when compared to crowdsourcing but no systematic analysis of the impact of system parameters on positioning accuracy is available. This paper fills this gap by introducing ViFi, an indoor positioning system that relies on RSS prediction based on Multi-Wall Multi-Floor (MWMF) propagation model to generate a discrete RSS radiomap (virtual fingerprints). The ViFi system is subject to an extensive experimental analysis in order to address the role of all relevant system parameters. Experimental results obtained in two different testbeds show that the introduction of virtual fingerprints allows reduction by a factor of 10 of the number of measurements, without significant loss in positioning accuracy. The use of two testbeds also allows to derive general guidelines for the design and the implementation of a virtual fingerprinting system.

Connected through hyperedge HE-1 (induced by source paper "hapi a robust pseudo 3d calibration free wifi based indoor localization system"):
  with members:
    - "a scalable deep neural network architecture for multi building and multi floor indoor localization based on wi fi fingerprinting"
    - "csi based fingerprinting for indoor localization a deep learning approach"
    - "selective ap sequence based indoor localization without site survey"
    - "a robust zero calibration rf based localization system for realistic environments"
    - "lanequest an accurate and energy efficient lane detection system"
    - "an analysis of device free and device based wifi localization systems"
    - "adam a method for stochastic optimization"
    - "map a crowd sensing system for automatic map semantics identification"

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)