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: "inertial odometry on handheld smartphones"
  Abstract: Building a complete inertial navigation system using the limited quality data provided by current smartphones has been regarded challenging, if not impossible. This paper shows that by careful crafting and accounting for the weak information in the sensor samples, smartphones are capable of pure inertial navigation. We present a probabilistic approach for orientation and use-case free inertial odometry, which is based on double-integrating rotated accelerations. The strength of the model is in learning additive and multiplicative IMU biases online. We are able to track the phone position, velocity, and pose in real-time and in a computationally lightweight fashion by solving the inference with an extended Kalman filter. The information fusion is completed with zero-velocity updates (if the phone remains stationary), altitude correction from barometric pressure readings (if available), and pseudo-updates constraining the momentary speed. We demonstrate our approach using an iPad and iPhone in several indoor dead-reckoning applications and in a measurement tool setup.

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 "calibration free positioning technique using wi fi and built in sensors of mobile devices"):
  with members:
    - "unsupervised learning technique to obtain the coordinates of wi fi access points"

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"
    - "ridi robust imu double integration"
    - "pivo probabilistic inertial visual odometry for occlusion robust navigation"

Connected through hyperedge HE-5 (induced by source paper "gyroscope aided motion deblurring with deep networks"):
  with members:
    - "reblur2deblur deblurring videos via self supervised learning"
    - "fast motion deblurring for feature detection and matching using inertial measurements"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "from motion blur to motion flow a deep learning solution for removing heterogeneous motion blur"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "advio an authentic dataset for visual inertial odometry"):
  with members:
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "pivo probabilistic inertial visual odometry for occlusion robust navigation"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "visual inertial monocular slam with map reuse"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "direct sparse odometry"
    - "imagenet large scale visual recognition challenge"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-7 (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"
    - "ridi robust imu double integration"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "robust gyroscope aided camera self calibration"):
  with members:
    - "pivo probabilistic inertial visual odometry for occlusion robust navigation"

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)