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 "iterative path reconstruction for large scale inertial navigation on smartphones"):
  Abstract: Modern smartphones have all the sensing capabilities required for accurate and robust navigation and tracking. In specific environments some data streams may be absent, less reliable, or flat out wrong. In particular, the GNSS signal can become flawed or silent inside buildings or in streets with tall buildings. In this application paper, we aim to advance the current state-of-the-art in motion estimation using inertial measurements in combination with partial GNSS data on standard smartphones. We show how iterative estimation methods help refine the positioning path estimates in retrospective use cases that can cover both fixed-interval and fixed-lag scenarios. We compare estimation results provided by global iterated Kalman filtering methods to those of a visual-inertial tracking scheme (Apple ARKit). The practical applicability is demonstrated on real-world use cases on empirical data acquired from both smartphones and tablet devices.

Includes members:
  Member "advio an authentic dataset for visual inertial odometry":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning based pedestrian inertial navigation methods dataset and on device inference")
  Member "ionet learning to cure the curse of drift in inertial odometry":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "denoising imu gyroscopes with deep learning for open loop attitude estimation")
  Member "deep learning based speed estimation for constraining strapdown inertial navigation on smartphones":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning based pedestrian inertial navigation methods dataset and on device inference")
  Member "ridi robust imu double integration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "denoising imu gyroscopes with deep learning for open loop attitude estimation")
  Member "pivo probabilistic inertial visual odometry for occlusion robust navigation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi view stereo by temporal nonparametric fusion")
  Member "inertial odometry on handheld smartphones":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "denoising imu gyroscopes with deep learning for open loop attitude estimation")

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.CV(Computer Vision and Pattern Recognition)