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: "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc"
  Abstract: We introduce Tempered Geodesic Markov Chain Monte Carlo (TG-MCMC) algorithm for initializing pose graph optimization problems, arising in various scenarios such as SFM (structure from motion) or SLAM (simultaneous localization and mapping). TG-MCMC is first of its kind as it unites asymptotically global non-convex optimization on the spherical manifold of quaternions with posterior sampling, in order to provide both reliable initial poses and uncertainty estimates that are informative about the quality of individual solutions. We devise rigorous theoretical convergence guarantees for our method and extensively evaluate it on synthetic and real benchmark datasets. Besides its elegance in formulation and theory, we show that our method is robust to missing data, noise and the estimated uncertainties capture intuitive properties of the data.

Connected through hyperedge HE-1 (induced by source paper "adversarial joint image and pose distribution learning for camera pose regression and refinement"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "3d human pose estimation in the wild by adversarial learning"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"

Connected through hyperedge HE-2 (induced by source paper "probabilistic permutation synchronization using the riemannian structure of the birkhoff polytope"):
  with members:
    - "synchronisation of partial multi matchings via non negative factorisations"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "averaging stochastic gradient descent on riemannian manifolds"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "cad priors for accurate and flexible instance reconstruction"
    - "consistent multiple graph matching with multi layer random walks synchronization"
    - "multi image semantic matching by mining consistent features"
    - "distributable consistent multi object matching"

Connected through hyperedge HE-3 (induced by source paper "3d local features for direct pairwise registration"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "latent ransac"
    - "bop benchmark for 6d object pose estimation"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "guaranteed outlier removal for point cloud registration with correspondences"
    - "cubenet equivariance to 3d rotation and translation"

Connected through hyperedge HE-4 (induced by source paper "improved pose graph optimization for planar motions using riemannian geometry on the manifold of dual quaternions"):
  with members:
    - "camera pose filtering with local regression geodesics on the riemannian manifold of dual quaternions"
    - "from angular manifolds to the integer lattice guaranteed orientation estimation with application to pose graph 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)