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: "modern wlan fingerprinting indoor positioning methods and deployment challenges"
  Abstract: Wireless Local Area Network (WLAN) has become a promising choice for indoor positioning as the only existing and established infrastructure, to localize the mobile and stationary users indoors. However, since WLAN has been initially designed for wireless networking and not positioning, the localization task based on WLAN signals has several challenges. Amongst the WLAN positioning methods, WLAN fingerprinting localization has recently achieved great attention due to its promising results. WLAN fingerprinting faces several challenges and hence, in this paper, our goal is to overview these challenges and the state-of-the-art solutions. This paper consists of three main parts: 1) Conventional localization schemes; 2) State-of-the-art approaches; 3) Practical deployment challenges. Since all the proposed methods in WLAN literature have been conducted and tested in different settings, the reported results are not equally comparable. So, we compare some of the main localization schemes in a single real environment and assess their localization accuracy, positioning error statistics, and complexity. Our results depict illustrative evaluation of WLAN localization systems and guide to future improvement opportunities.

Connected through hyperedge HE-1 (induced by source paper "collaborative slam based on wifi fingerprint similarity and motion information"):
  with members:
    - "simultaneous merging multiple grid maps using the robust motion averaging"
    - "cooperative relative positioning of mobile users by fusing imu inertial and uwb ranging information"
    - "rfm slam exploiting relative feature measurements to separate orientation and position estimation in slam"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-2 (induced by source paper "csi based outdoor localization for massive mimo experiments with a learning approach"):
  with members:
    - "multi band covariance interpolation with applications in massive mimo"
    - "channel covariance estimation in massive mimo frequency division duplex systems"
    - "fingerprinting based positioning in distributed massive mimo systems"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-3 (induced by source paper "an implementation approach and performance analysis of image sensor based multilateral indoor localization and navigation system"):
  with members:
    - "a new vehicle localization scheme based on combined optical camera communication and photogrammetry"
    - "a comparative survey of optical wireless technologies architectures and applications"
    - "csi based fingerprinting for indoor localization a deep learning approach"

Connected through hyperedge HE-4 (induced by source paper "a fast rate wlan measurement tool for improved miss rate in indoor navigation"):
  with members:
    - "a joint indoor wlan localization and outlier detection scheme using lasso and elastic net optimization techniques"
    - "csi based fingerprinting for indoor localization a deep learning approach"

Connected through hyperedge HE-5 (induced by source paper "structured group sparsity a novel indoor wlan localization outlier detection and radio map interpolation scheme"):
  with members:
    - "a joint indoor wlan localization and outlier detection scheme using lasso and elastic net optimization techniques"

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)