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: "a micro lie theory for state estimation in robotics"
  Abstract: A Lie group is an old mathematical abstract object dating back to the XIX century, when mathematician Sophus Lie laid the foundations of the theory of continuous transformation groups. As it often happens, its usage has spread over diverse areas of science and technology many years later. In robotics, we are recently experiencing an important trend in its usage, at least in the fields of estimation, and particularly in motion estimation for navigation. Yet for a vast majority of roboticians, Lie groups are highly abstract constructions and therefore difficult to understand and to use. This may be due to the fact that most of the literature on Lie theory is written by and for mathematicians and physicists, who might be more used than us to the deep abstractions this theory deals with. #R##N#In estimation for robotics it is often not necessary to exploit the full capacity of the theory, and therefore an effort of selection of materials is required. In this paper, we will walk through the most basic principles of the Lie theory, with the aim of conveying clear and useful ideas, and leave a significant corpus of the Lie theory behind. Even with this mutilation, the material included here has proven to be extremely useful in modern estimation algorithms for robotics, especially in the fields of SLAM, visual odometry, and the like. #R##N#Alongside this micro Lie theory, we provide a chapter with a few application examples, and a vast reference of formulas for the major Lie groups used in robotics, including most jacobian matrices and the way to easily manipulate them. We also present a new C++ template-only library implementing all the functionality described here.

Connected through hyperedge HE-1 (induced by source paper "self supervised deep pose corrections for robust visual odometry"):
  with members:
    - "does computer vision matter for action"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "robustness meets deep learning an end to end hybrid pipeline for unsupervised learning of egomotion"
    - "sfmlearner learning monocular depth ego motion using meaningful geometric constraints"
    - "geometric consistency for self supervised end to end visual odometry"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "dpc net deep pose correction for visual localization"

Connected through hyperedge HE-2 (induced by source paper "a code for unscented kalman filtering on manifolds ukf m"):
  with members:
    - "exploiting symmetries to design ekfs with consistency properties for navigation and slam"
    - "contact aided invariant extended kalman filtering for legged robot state estimation"
    - "an ekf slam algorithm with consistency properties"
    - "quaternion kinematics for the error state kalman filter"
    - "the invariant extended kalman filter as a stable observer"
    - "intrinsic filtering on lie groups with applications to attitude estimation"
    - "integrating generic sensor fusion algorithms with sound state representations through encapsulation of manifolds"

Connected through hyperedge HE-3 (induced by source paper "rgb d odometry and slam"):
  with members:
    - "cam convs camera aware multi scale convolutions for single view depth"
    - "direct sparse mapping"
    - "deeptam deep tracking and mapping"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "planematch patch coplanarity prediction for robust rgb d reconstruction"
    - "rgbid slam for accurate real time localisation and 3d mapping"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"

Connected through hyperedge HE-4 (induced by source paper "probabilistic regression of rotations using quaternion averaging and a deep multi headed network"):
  with members:
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "dpc net deep pose correction for visual localization"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "unsupervised learning of depth and ego motion from video"
    - "relative camera pose estimation using convolutional neural networks"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "gaussians on riemannian manifolds for robot learning and adaptive control"):
  with members:
    - "rmpflow a computational graph for automatic motion policy generation"
    - "the vector heat method"
    - "geometry aware manipulability learning tracking and transfer"
    - "on manifold preintegration for real time visual inertial odometry"
    - "identification of fully physical consistent inertial parameters using optimization on manifolds"
    - "trajectory synthesis for fisher information maximization"

Connected through hyperedge HE-6 (induced by source paper "efficient derivative computation for cumulative b splines on lie groups"):
  with members:
    - "continuous time visual inertial odometry for event cameras"
    - "trajectory representation and landmark projection for continuous time structure from motion"
    - "spline error weighting for robust visual inertial fusion"
    - "real time trajectory replanning for mavs using uniform b splines and a 3d circular buffer"

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.RO(Robotics)