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: "pivo probabilistic inertial visual odometry for occlusion robust navigation"
  Abstract: This paper presents a novel method for visual-inertial odometry. The method is based on an information fusion framework employing low-cost IMU sensors and the monocular camera in a standard smartphone. We formulate a sequential inference scheme, where the IMU drives the dynamical model and the camera frames are used in coupling trailing sequences of augmented poses. The novelty in the model is in taking into account all the cross-terms in the updates, thus propagating the inter-connected uncertainties throughout the model. Stronger coupling between the inertial and visual data sources leads to robustness against occlusion and feature-poor environments. We demonstrate results on data collected with an iPhone and provide comparisons against the Tango device and using the EuRoC data set.

Connected through hyperedge HE-1 (induced by source paper "multi view stereo by temporal nonparametric fusion"):
  with members:
    - "unstructured multi view depth estimation using mask based multiplane representation"
    - "dpsnet end to end deep plane sweep stereo"
    - "deeptam deep tracking and mapping"
    - "deepmvs learning multi view stereopsis"
    - "gaussian process prior variational autoencoders"
    - "mvdepthnet real time multiview depth estimation neural network"
    - "learning a multi view stereo machine"
    - "demon depth and motion network for learning monocular stereo"

Connected through hyperedge HE-2 (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"
    - "inertial odometry on handheld smartphones"

Connected through hyperedge HE-3 (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"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "visual inertial monocular slam with map reuse"
    - "inertial odometry on handheld smartphones"
    - "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-4 (induced by source paper "robust gyroscope aided camera self calibration"):
  with members:
    - "inertial odometry on handheld smartphones"

Connected through hyperedge HE-5 (induced by source paper "inertial odometry on handheld smartphones"):
  with members:
    - "modeling and interpolation of the ambient magnetic field by gaussian processes"
    - "cooperative localization by dual foot mounted inertial sensors and inter agent ranging"

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)