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: "on computing the translations norm in the epipolar graph"
  Abstract: This paper deals with the problem of recovering the unknown norm of relative translations between cameras based on the knowledge of relative rotations and translation directions. We provide theoretical conditions for the solvability of such a problem, and we propose a two-stage method to solve it. First, a cycle basis for the epipolar graph is computed, then all the scaling factors are recovered simultaneously by solving a homogeneous linear system. We demonstrate the accuracy of our solution by means of synthetic and real experiments.

Connected through hyperedge HE-1 (induced by source paper "robust rotation synchronization via low rank and sparse matrix decomposition"):
  with members:
    - "a survey of structure from motion"
    - "shapefit and shapekick for robust scalable structure from motion"
    - "spectral synchronization of multiple views in se 3"
    - "practical matrix completion and corruption recovery using proximal alternating robust subspace minimization"
    - "a solution for multi alignment by transformation synchronisation"
    - "cramer rao bounds for synchronization of rotations"
    - "robust camera location estimation by convex programming"
    - "low rank modeling and its applications in image analysis"

Connected through hyperedge HE-2 (induced by source paper "robust sfm with little image overlap"):
  with members:
    - "hierarchical structure and motion recovery from uncalibrated images"
    - "linear global translation estimation with feature tracks"
    - "robust camera location estimation by convex programming"

Connected through hyperedge HE-3 (induced by source paper "spectral synchronization of multiple views in se 3"):
  with members:
    - "stable camera motion estimation using convex programming"
    - "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)