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: "an analysis of device free and device based wifi localization systems"
  Abstract: WiFi-based localization became one of the main indoor localization techniques due to the ubiquity of WiFi connectivity. However, indoor environments exhibit complex wireless propagation characteristics. Typically, these characteristics are captured by constructing a fingerprint map for the different locations in the area of interest. This finger print requires significant overhead in manual construction, and thus has been one of the major drawbacks of WiFi-based localization. In this paper, the authors present an automated tool for finger print constructions and leverage it to study novel scenarios for device-based and device-free WiFi-based localization that are difficult to evaluate in a real environment. In a particular, the authors examine the effect of changing the access points AP mounting location, AP technology upgrade, crowd effect on calibration and operation, among others; on the accuracy of the localization system. The authors present the analysis for the two classes of WiFi-based localization: device-based and device-free. The authors analysis highlights factors affecting the localization system accuracy, how to tune it for better localization, and provides insights for both researchers and practitioners.

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"
    - "vifi virtual fingerprinting wifi based indoor positioning via multi wall multi floor propagation model"
    - "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"
    - "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)