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: "lagrangian duality in 3d slam verification techniques and optimal solutions"
  Abstract: State-of-the-art techniques for simultaneous localization and mapping (SLAM) employ iterative nonlinear optimization methods to compute an estimate for robot poses. While these techniques often work well in practice, they do not provide guarantees on the quality of the estimate. This paper shows that Lagrangian duality is a powerful tool to assess the quality of a given candidate solution. Our contribution is threefold. First, we discuss a revised formulation of the SLAM inference problem. We show that this formulation is probabilistically grounded and has the advantage of leading to an optimization problem with quadratic objective. The second contribution is the derivation of the corresponding Lagrangian dual problem. The SLAM dual problem is a (convex) semidefinite program, which can be solved reliably and globally by off-the-shelf solvers. The third contribution is to discuss the relation between the original SLAM problem and its dual. We show that from the dual problem, one can evaluate the quality (i.e., the suboptimality gap) of a candidate SLAM solution, and ultimately provide a certificate of optimality. Moreover, when the duality gap is zero, one can compute a guaranteed optimal SLAM solution from the dual problem, circumventing non-convex optimization. We present extensive (real and simulated) experiments supporting our claims and discuss practical relevance and open problems.

Connected through hyperedge HE-1 (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"
    - "a micro lie theory for state estimation in robotics"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "graduated non convexity for robust spatial perception from non minimal solvers to global outlier rejection"):
  with members:
    - "a polynomial time solution for robust registration with extreme outlier rates"
    - "cvxpnpl a unified convex solution to the absolute pose estimation problem from point and line correspondences"
    - "convex relaxations for pose graph optimization with outliers"
    - "accelerated inference in markov random fields via smooth riemannian optimization"
    - "modeling perceptual aliasing in slam via discrete continuous graphical models"
    - "guaranteed outlier removal for point cloud registration with correspondences"
    - "robust fitting in computer vision easy or hard"
    - "rotation averaging and strong duality"

Connected through hyperedge HE-3 (induced by source paper "certifiably globally optimal extrinsic calibration from per sensor egomotion"):
  with members:
    - "self calibration of mobile manipulator kinematic and sensor extrinsic parameters through contact based interaction"
    - "rotation averaging and strong duality"
    - "entropy based sim 3 calibration of 2d lidars to egomotion sensors"
    - "a certifiably correct algorithm for synchronization over the special euclidean group"
    - "efficient global point cloud alignment using bayesian nonparametric mixtures"
    - "hand eye and robot world calibration by global polynomial optimization"

Connected through hyperedge HE-4 (induced by source paper "visual slam why bundle adjust"):
  with members:
    - "rotation averaging and strong duality"
    - "gslam initialization robust monocular visual slam via global structure from motion"
    - "a discrete time attitude observer on so 3 for vision and gps fusion"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-5 (induced by source paper "modeling perceptual aliasing in slam via discrete continuous graphical models"):
  with members:
    - "convex relaxations for pose graph optimization with outliers"
    - "robust rotation synchronization via low rank and sparse matrix decomposition"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "exact and stable recovery of rotations for robust synchronization"

Connected through hyperedge HE-6 (induced by source paper "convex relaxations for pose graph optimization with outliers"):
  with members:
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "exact and stable recovery of rotations for robust synchronization"
    - "from angular manifolds to the integer lattice guaranteed orientation estimation with application to pose graph optimization"

Connected through hyperedge HE-7 (induced by source paper "guaranteed globally optimal planar pose graph and landmark slam via sparse bounded sums of squares programming"):
  with members:
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-8 (induced by source paper "distributed mapping with privacy and communication constraints lightweight algorithms and object based models"):
  with members:
    - "semanticpaint interactive 3d labeling and learning at your fingertips"
    - "monocular slam supported object recognition"
    - "real time monocular object slam"
    - "distributed maximum likelihood sensor network localization"

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)