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: "ridi robust imu double integration"
  Abstract: This paper proposes a novel data-driven approach for inertial navigation, which learns to estimate trajectories of natural human motions just from an inertial measurement unit (IMU) in every smartphone. The key observation is that human motions are repetitive and consist of a few major modes (e.g., standing, walking, or turning). Our algorithm regresses a velocity vector from the history of linear accelerations and angular velocities, then corrects low-frequency bias in the linear accelerations, which are integrated twice to estimate positions. We have acquired training data with ground-truth motions across multiple human subjects and multiple phone placements (e.g., in a bag or a hand). The qualitatively and quantitatively evaluations have demonstrated that our algorithm has surprisingly shown comparable results to full Visual Inertial navigation. To our knowledge, this paper is the first to integrate sophisticated machine learning techniques with inertial navigation, potentially opening up a new line of research in the domain of data-driven inertial navigation. We will publicly share our code and data to facilitate further research.

Connected through hyperedge HE-1 (induced by source paper "denoising imu gyroscopes with deep learning for open loop attitude estimation"):
  with members:
    - "visual inertial odometry of aerial robots"
    - "ronin robust neural inertial navigation in the wild benchmark evaluations and new methods"
    - "rins w robust inertial navigation system on wheels"
    - "ai imu dead reckoning"
    - "selfvio self supervised deep monocular visual inertial odometry and depth estimation"
    - "a mathematical framework for imu error propagation with applications to preintegration"
    - "using inertial sensors for position and orientation estimation"
    - "ionet learning to cure the curse of drift in inertial odometry"

Connected through hyperedge HE-2 (induced by source paper "deep learning based pedestrian inertial navigation methods dataset and on device inference"):
  with members:
    - "advio an authentic dataset for visual inertial odometry"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "lstm based zero velocity detection for robust inertial navigation"
    - "deep learning based speed estimation for constraining strapdown inertial navigation on smartphones"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "lstm a search space odyssey"

Connected through hyperedge HE-3 (induced by source paper "rins w robust inertial navigation system on wheels"):
  with members:
    - "contact aided invariant extended kalman filtering for legged robot state estimation"
    - "imls slam scan to model matching based on 3d data"
    - "using inertial sensors for position and orientation estimation"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "lstm based zero velocity detection for robust inertial navigation"
    - "deep learning based speed estimation for constraining strapdown inertial navigation on smartphones"
    - "complex urban lidar data set"
    - "oxiod the dataset for deep inertial odometry"

Connected through hyperedge HE-4 (induced by source paper "iterative path reconstruction for large scale inertial navigation on smartphones"):
  with members:
    - "advio an authentic dataset for visual inertial odometry"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "deep learning based speed estimation for constraining strapdown inertial navigation on smartphones"
    - "pivo probabilistic inertial visual odometry for occlusion robust navigation"
    - "inertial odometry on handheld smartphones"

Connected through hyperedge HE-5 (induced by source paper "deep learning based speed estimation for constraining strapdown inertial navigation on smartphones"):
  with members:
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "advio an authentic dataset for visual inertial odometry"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "inertial odometry on handheld smartphones"
    - "adam a method for stochastic optimization"

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.CV(Computer Vision and Pattern Recognition)