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 "a certifiably correct algorithm for synchronization over the special euclidean group"):
  Abstract: Many geometric estimation problems take the form of synchronization over the special Euclidean group: estimate the values of a set of poses given noisy measurements of a subset of their pairwise relative transforms. This problem is typically formulated as a maximum-likelihood estimation that requires solving a nonconvex nonlinear program, which is computationally intractable in general. Nevertheless, in this paper we present an algorithm that is able to efficiently recover certifiably globally optimal solutions of this estimation problem in a non-adversarial noise regime. The crux of our approach is the development of a semidefinite relaxation of the maximum-likelihood estimation whose minimizer provides the exact MLE so long as the magnitude of the noise corrupting the available measurements falls below a certain critical threshold; furthermore, whenever exactness obtains, it is possible to verify this fact a posteriori, thereby certifying the optimality of the recovered estimate. We develop a specialized optimization scheme for solving large-scale instances of this semidefinite relaxation by exploiting its low-rank, geometric, and graph-theoretic structure to reduce it to an equivalent optimization problem on a low-dimensional Riemannian manifold, and then design a Riemannian truncated-Newton trust-region method to solve this reduction efficiently. We combine this fast optimization approach with a simple rounding procedure to produce our algorithm, SE-Sync. Experimental evaluation on a variety of simulated and real-world pose-graph SLAM datasets shows that SE-Sync is capable of recovering globally optimal solutions when the available measurements are corrupted by noise up to an order of magnitude greater than that typically encountered in robotics applications, and does so at a computational cost that scales comparably with that of direct Newton-type local search techniques.

Includes members:
  Member "spectral synchronization of multiple views in se 3":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning multiview 3d point cloud registration")
  Member "non unique games over compact groups and orientation estimation in cryo em":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "single particle cryo electron microscopy mathematical theory computational challenges and opportunities")
  Member "cramer rao bounds for synchronization of rotations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the distribution of minima in intrinsic metric rotation averaging")
  Member "manopt a matlab toolbox for optimization on manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint constellation design for the two user non coherent multiple access channel")
  Member "exact and stable recovery of rotations for robust synchronization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the distribution of minima in intrinsic metric rotation averaging")
  Member "from angular manifolds to the integer lattice guaranteed orientation estimation with application to pose graph optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improved pose graph optimization for planar motions using riemannian geometry on the manifold of dual quaternions")
  Member "stable principal component pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")
  Member "robust principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

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.RO(Robotics)