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 "denoising imu gyroscopes with deep learning for open loop attitude estimation"):
  Abstract: This paper proposes a learning method for denois-ing gyroscopes of Inertial Measurement Units (IMUs) using ground truth data, to estimate in real time the orientation (attitude) of a robot in dead reckoning. The obtained algorithm outperforms the state-of-the-art on the (unseen) test sequences. The obtained performances are achieved thanks to a well chosen model, a proper loss function for orientation increments, and through the identification of key points when training with high-frequency inertial data. Our approach builds upon a neural network based on dilated convolutions, without requiring any recurrent neural network. We demonstrate how efficient our strategy is for 3D attitude estimation on the EuRoC and TUM-VI datasets. Interestingly, we observe our dead reckoning algorithm manages to beat top-ranked visual-inertial odometry systems in terms of attitude estimation although it does not use vision sensors. We believe this paper offers new perspectives for visual-inertial localization and constitutes a step toward more efficient learning methods involving IMUs. Our open-source implementation is available at https://github.com/ mbrossar/denoise-imu-gyro.

Includes members:
  Member "visual inertial odometry of aerial robots":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tightly coupled fusion of global positional measurements in optimization based visual inertial odometry")
  Member "ronin robust neural inertial navigation in the wild benchmark evaluations and new methods":
    also connected through other hyperedges:
      (none)
  Member "rins w robust inertial navigation system on wheels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "contact aided invariant extended kalman filtering for legged robot state estimation")
  Member "ai imu dead reckoning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nuscenes a multimodal dataset for autonomous driving")
  Member "selfvio self supervised deep monocular visual inertial odometry and depth estimation":
    also connected through other hyperedges:
      (none)
  Member "a mathematical framework for imu error propagation with applications to preintegration":
    also connected through other hyperedges:
      (none)
  Member "using inertial sensors for position and orientation estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a review of computational approaches for evaluation of rehabilitation exercises")
  Member "ionet learning to cure the curse of drift in inertial odometry":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning based pedestrian inertial navigation methods dataset and on device inference")

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.RO(Robotics)