human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "convex relaxations for pose graph optimization with outliers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convex optimisation for inverse kinematics")
  Member "local optimality and generalization guarantees for the langevin algorithm via empirical metastability":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on generalization error bounds of noisy gradient methods for non convex learning")
  Member "rotation averaging and strong duality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the distribution of minima in intrinsic metric rotation averaging")
  Member "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an empirical study of large batch stochastic gradient descent with structured covariance noise")
  Member "a hitting time analysis of stochastic gradient langevin dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "cad priors for accurate and flexible instance reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning multiview 3d point cloud registration")
  Member "camera pose filtering with local regression geodesics on the riemannian manifold of dual quaternions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quaternion equivariant capsule networks for 3d point clouds")
  Member "a certifiably correct algorithm for synchronization over the special euclidean group":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "certifiably globally optimal extrinsic calibration from per sensor egomotion")

Question: Please tell me which class the source paper 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)