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 "graduated non convexity for robust spatial perception from non minimal solvers to global outlier rejection"):
  Abstract: Semidefinite Programming (SDP) and Sums-of-Squares (SOS) relaxations have led to certifiably optimal non-minimal solvers for several robotics and computer vision problems. However, most non-minimal solvers rely on least-squares formulations, and, as a result, are brittle against outliers. While a standard approach to regain robustness against outliers is to use robust cost functions, the latter typically introduce other non-convexities, preventing the use of existing non-minimal solvers. In this paper, we enable the simultaneous use of non-minimal solvers and robust estimation by providing a general-purpose approach for robust global estimation, which can be applied to any problem where a non-minimal solver is available for the outlier-free case. To this end, we leverage the Black-Rangarajan duality between robust estimation and outlier processes (which has been traditionally applied to early vision problems), and show that graduated non-convexity (GNC) can be used in conjunction with non-minimal solvers to compute robust solutions, without requiring an initial guess. Although GNC's global optimality cannot be guaranteed, we demonstrate the empirical robustness of the resulting robust non-minimal solvers in applications, including point cloud and mesh registration, pose graph optimization, and image-based object pose estimation (also called shape alignment). Our solvers are robust to 70-80% of outliers, outperform RANSAC, are more accurate than specialized local solvers, and faster than specialized global solvers. We also propose the first certifiably optimal non-minimal solver for shape alignment using SOS relaxation.

Includes members:
  Member "a polynomial time solution for robust registration with extreme outlier rates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "teaser fast and certifiable point cloud registration")
  Member "cvxpnpl a unified convex solution to the absolute pose estimation problem from point and line correspondences":
    also connected through other hyperedges:
      (none)
  Member "convex relaxations for pose graph optimization with outliers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convex optimisation for inverse kinematics")
  Member "accelerated inference in markov random fields via smooth riemannian optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "kimera an open source library for real time metric semantic localization and mapping")
  Member "modeling perceptual aliasing in slam via discrete continuous graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "teaser fast and certifiable point cloud registration")
  Member "guaranteed outlier removal for point cloud registration with correspondences":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "teaser fast and certifiable point cloud registration")
  Member "robust fitting in computer vision easy or hard":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "teaser fast and certifiable point cloud registration")
  Member "rotation averaging and strong duality":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the distribution of minima in intrinsic metric rotation averaging")

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)