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: "linear global translation estimation with feature tracks"
  Abstract: This paper derives a novel linear position constraint for cameras seeing a common scene point, which leads to a direct linear method for global camera translation estimation. Unlike previous solutions, this method deals with collinear camera motion and weak image association at the same time. The final linear formulation does not involve the coordinates of scene points, which makes it efficient even for large scale data. We solve the linear equation based on $L_1$ norm, which makes our system more robust to outliers in essential matrices and feature correspondences. We experiment this method on both sequentially captured images and unordered Internet images. The experiments demonstrate its strength in robustness, accuracy, and efficiency.

Connected through hyperedge HE-1 (induced by source paper "averaging essential and fundamental matrices in collinear camera settings"):
  with members:
    - "resultant based incremental recovery of camera pose from pairwise matches"
    - "algebraic characterization of essential matrices and their averaging in multiview settings"
    - "on the solvability of viewing graphs"
    - "gpsfm global projective sfm using algebraic constraints on multi view fundamental matrices"
    - "a new rank constraint on multi view fundamental matrices and its application to camera location recovery"
    - "robust camera location estimation by convex programming"

Connected through hyperedge HE-2 (induced by source paper "algebraic characterization of essential matrices and their averaging in multiview settings"):
  with members:
    - "resultant based incremental recovery of camera pose from pairwise matches"
    - "gpsfm global projective sfm using algebraic constraints on multi view fundamental matrices"
    - "rotation averaging and strong duality"
    - "a new rank constraint on multi view fundamental matrices and its application to camera location recovery"
    - "shapefit and shapekick for robust scalable structure from motion"
    - "robust camera location estimation by convex programming"

Connected through hyperedge HE-3 (induced by source paper "parallax bundle adjustment on manifold with convexified initialization"):
  with members:
    - "gslam initialization robust monocular visual slam via global structure from motion"

Connected through hyperedge HE-4 (induced by source paper "robust sfm with little image overlap"):
  with members:
    - "on computing the translations norm in the epipolar graph"
    - "hierarchical structure and motion recovery from uncalibrated images"
    - "robust camera location estimation by convex programming"

Connected through hyperedge HE-5 (induced by source paper "parallel structure from motion from local increment to global averaging"):
  with members:
    - "large scale sfm with the distributed camera model"
    - "hierarchical structure and motion recovery from uncalibrated images"
    - "robust camera location estimation by convex programming"

Connected through hyperedge HE-6 (induced by source paper "exact simultaneous recovery of locations and structure from known orientations and corrupted point correspondences"):
  with members:
    - "shapefit and shapekick for robust scalable structure from motion"
    - "shapefit exact location recovery from corrupted pairwise directions"
    - "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)