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: "robust rotation synchronization via low rank and sparse matrix decomposition"
  Abstract: This paper deals with the rotation synchronization problem, which arises in global registration of 3D point-sets and in structure from motion. The problem is formulated in an unprecedented way as a \low-rank and sparse" matrix decomposition that handles both outliers and missing data. A minimization strategy, dubbed R-GoDec, is also proposed and evaluated experimentally against state-of-the-art algorithms on simulated and real data. The results show that R-GoDec is the fastest among the robust algorithms.

Connected through hyperedge HE-1 (induced by source paper "learning transformation synchronization"):
  with members:
    - "open3d a modern library for 3d data processing"
    - "recurrent transformer networks for semantic correspondence"
    - "learning to find good correspondences"
    - "spectral synchronization of multiple views in se 3"
    - "a large dataset of object scans"
    - "a solution for multi alignment by transformation synchronisation"
    - "multi image matching via fast alternating minimization"
    - "near optimal joint object matching via convex relaxation"

Connected through hyperedge HE-2 (induced by source paper "path invariant map networks"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "scannet richly annotated 3d reconstructions of indoor scenes"
    - "data distillation towards omni supervised learning"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "dualgan unsupervised dual learning for image to image translation"
    - "semantic scene completion from a single depth image"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-3 (induced by source paper "modeling perceptual aliasing in slam via discrete continuous graphical models"):
  with members:
    - "convex relaxations for pose graph optimization with outliers"
    - "lagrangian duality in 3d slam verification techniques and optimal solutions"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "exact and stable recovery of rotations for robust synchronization"

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)