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: "from angular manifolds to the integer lattice guaranteed orientation estimation with application to pose graph optimization"
  Abstract: Estimating the orientations of nodes in a pose graph from relative angular measurements is challenging because the variables live on a manifold product with nontrivial topology and the maximum-likelihood objective function is non-convex and has multiple local minima; these issues prevent iterative solvers to be robust for large amounts of noise. This paper presents an approach that allows working around the problem of multiple minima, and is based on the insight that the original estimation problem on orientations is equivalent to an unconstrained quadratic optimization problem on integer vectors. This equivalence provides a viable way to compute the maximum likelihood estimate and allows guaranteeing that such estimate is almost surely unique. A deeper consequence of the derivation is that the maximum likelihood solution does not necessarily lead to an estimate that is "close" to the actual nodes orientations, hence it is not necessarily the best choice for the problem at hand. To alleviate this issue, our algorithm computes a set of estimates, for which we can derive precise probabilistic guarantees. Experiments show that the method is able to tolerate extreme amounts of noise (e.g., {\sigma} = 30{\deg} on each measurement) that are above all noise levels of sensors commonly used in mapping. For most range-finder-based scenarios, the multi-hypothesis estimator returns only a single hypothesis, because the problem is very well constrained. Finally, using the orientations estimate provided by our method to bootstrap the initial guess of pose graph optimization methods improves their robustness and makes them avoid local minima even for high levels of noise.

Connected through hyperedge HE-1 (induced by source paper "improved pose graph optimization for planar motions using riemannian geometry on the manifold of dual quaternions"):
  with members:
    - "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc"
    - "camera pose filtering with local regression geodesics on the riemannian manifold of dual quaternions"

Connected through hyperedge HE-2 (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"
    - "lagrangian duality in 3d slam verification techniques and optimal solutions"
    - "exact and stable recovery of rotations for robust synchronization"

Connected through hyperedge HE-3 (induced by source paper "linear slam linearising the slam problems using submap joining"):
  with members:
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-4 (induced by source paper "gslam initialization robust monocular visual slam via global structure from motion"):
  with members:
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "orb slam a versatile and accurate monocular slam system"
    - "robust camera location estimation by convex programming"

Connected through hyperedge HE-5 (induced by source paper "past present and future of simultaneous localization and mapping toward the robust perception age"):
  with members:
    - "on manifold preintegration for real time visual inertial odometry"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "event based 6 dof camera tracking for high speed applications"
    - "monte carlo localization in hand drawn maps"
    - "on manifold preintegration theory for fast and accurate visual inertial navigation"
    - "monocular slam supported object recognition"
    - "lagrangian duality in 3d slam verification techniques and optimal solutions"

Connected through hyperedge HE-6 (induced by source paper "a certifiably correct algorithm for synchronization over the special euclidean group"):
  with members:
    - "spectral synchronization of multiple views in se 3"
    - "non unique games over compact groups and orientation estimation in cryo em"
    - "cramer rao bounds for synchronization of rotations"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "exact and stable recovery of rotations for robust synchronization"
    - "stable principal component pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-7 (induced by source paper "rfm slam exploiting relative feature measurements to separate orientation and position estimation in slam"):
  with members:
    - "manopt a matlab toolbox for optimization on manifolds"

Connected through hyperedge HE-8 (induced by source paper "analytical slam without linearization"):
  with members:
    - "simultaneous localization and mapping without linearization"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "estimating uncertain spatial relationships in robotics"
    - "efficient touch based localization through submodularity"

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)