human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "hapi a robust pseudo 3d calibration free wifi based indoor localization system"):
  Abstract: In this paper, we present Hapi, a novel system that uses off-the-shelf standard WiFi to provide pseudo-3D indoor localization---it estimates the user's floor and her 2D location on that floor. Hapi is calibration-free, only requiring the building's floorplans and its WiFi APs' installation location for deployment. Our analysis shows that while a user can hear APs from nearby floors as well as her floor, she will typically only receive signals from spatially closer APs in distant floors, as compared to APs in her floor. This is due to signal attenuation by floors/ceilings along with the 3D distance between the APs and the user. Hapi leverages this observation to achieve accurate and robust location estimates.   A deep-learning based method is proposed to identify the user's floor. Then, the identified floor along with the user's visible APs from all floors are used to estimate her 2D location through a novel RSS-Rank Gaussian-based method. Additionally, we present a regression based method to predict Hapi's location estimates' quality and employ it within a Kalman Filter to further refine the accuracy. Our evaluation results, from deployment on various android devices over 6 months with 13 subjects in 5 different up to 9 floors multistory buildings, show that Hapi can identify the user's exact floor up to 95.2% of the time and her 2D location with a median accuracy of 3.5m, achieving 52.1% and 76.0% improvement over related calibration-free state-of-the-art systems respectively.

Includes members:
  Member "a scalable deep neural network architecture for multi building and multi floor indoor localization based on wi fi fingerprinting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "device free user authentication activity classification and tracking using passive wi fi sensing a deep learning based approach")
  Member "vifi virtual fingerprinting wifi based indoor positioning via multi wall multi floor propagation model":
    also connected through other hyperedges:
      (none)
  Member "csi based fingerprinting for indoor localization a deep learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "selective ap sequence based indoor localization without site survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "indoor positioning using similarity based sequence and dead reckoning without training")
  Member "a robust zero calibration rf based localization system for realistic environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "zero calibration device free localization for the iot based on participatory sensing")
  Member "lanequest an accurate and energy efficient lane detection system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "real time lane id estimation using recurrent neural networks with dual convention")
  Member "an analysis of device free and device based wifi localization systems":
    also connected through other hyperedges:
      (none)
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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.CY(Computers and Society)