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: "advio an authentic dataset for visual inertial odometry"
  Abstract: The lack of realistic and open benchmarking datasets for pedestrian visual-inertial odometry has made it hard to pinpoint differences in published methods. Existing datasets either lack a full six degree-of-freedom ground-truth or are limited to small spaces with optical tracking systems. We take advantage of advances in pure inertial navigation, and develop a set of versatile and challenging real-world computer vision benchmark sets for visual-inertial odometry. For this purpose, we have built a test rig equipped with an iPhone, a Google Pixel Android phone, and a Google Tango device. We provide a wide range of raw sensor data that is accessible on almost any modern-day smartphone together with a high-quality ground-truth track. We also compare resulting visual-inertial tracks from Google Tango, ARCore, and Apple ARKit with two recent methods published in academic forums. The data sets cover both indoor and outdoor cases, with stairs, escalators, elevators, office environments, a shopping mall, and metro station.

Connected through hyperedge HE-1 (induced by source paper "deep learning based pedestrian inertial navigation methods dataset and on device inference"):
  with members:
    - "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"
    - "ridi robust imu double integration"

Connected through hyperedge HE-2 (induced by source paper "brno urban dataset the new data for self driving agents and mapping tasks"):
  with members:
    - "self driving cars a survey"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "on the two view geometry of unsynchronized cameras"
    - "playing for benchmarks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "torontocity seeing the world with a million eyes"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-3 (induced by source paper "iterative path reconstruction for large scale inertial navigation on smartphones"):
  with members:
    - "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"
    - "inertial odometry on handheld smartphones"

Connected through hyperedge HE-4 (induced by source paper "the mobile ar sensor logger for android and ios devices"):
  with members:
    - "collaborative large scale dense 3d reconstruction with online inter agent pose optimisation"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "oxiod the dataset for deep inertial odometry"
    - "active user authentication for smartphones a challenge data set and benchmark results"

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"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "ridi robust imu double integration"
    - "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)