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: "five point fundamental matrix estimation for uncalibrated cameras"
  Abstract: We aim at estimating the fundamental matrix in two views from five correspondences of rotation invariant features obtained by e.g.\ the SIFT detector. The proposed minimal solver first estimates a homography from three correspondences assuming that they are co-planar and exploiting their rotational components. Then the fundamental matrix is obtained from the homography and two additional point pairs in general position. The proposed approach, combined with robust estimators like Graph-Cut RANSAC, is superior to other state-of-the-art algorithms both in terms of accuracy and number of iterations required. This is validated on synthesized data and $561$ real image pairs. Moreover, the tests show that requiring three points on a plane is not too restrictive in urban environment and locally optimized robust estimators lead to accurate estimates even if the points are not entirely co-planar. As a potential application, we show that using the proposed method makes two-view multi-motion estimation more accurate.

Connected through hyperedge HE-1 (induced by source paper "pl _ 1 p point line minimal problems under partial visibility in three views"):
  with members:
    - "trifocal relative pose from lines at points and its efficient solution"
    - "plmp point line minimal problems in complete multi view visibility"
    - "neighbourhood consensus networks"
    - "a minimal closed form solution for multi perspective pose estimation using points and lines"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "a clever elimination strategy for efficient minimal solvers"
    - "a minimal solution for two view focal length estimation using two affine correspondences"
    - "multiview differential geometry of curves"

Connected through hyperedge HE-2 (induced by source paper "plmp point line minimal problems in complete multi view visibility"):
  with members:
    - "trifocal relative pose from lines at points and its efficient solution"
    - "beyond gr obner bases basis selection for minimal solvers"
    - "neighbourhood consensus networks"
    - "a minimal closed form solution for multi perspective pose estimation using points and lines"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "a clever elimination strategy for efficient minimal solvers"
    - "a minimal solution for two view focal length estimation using two affine correspondences"
    - "multiview differential geometry of curves"

Connected through hyperedge HE-3 (induced by source paper "minimal solutions for relative pose with a single affine correspondence"):
  with members:
    - "homography from two orientation and scale covariant features"
    - "plmp point line minimal problems in complete multi view visibility"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "a minimal solution for two view focal length estimation using two affine correspondences"
    - "on the existence of epipolar matrices"
    - "mods fast and robust method for two view matching"

Connected through hyperedge HE-4 (induced by source paper "homography from two orientation and scale covariant features"):
  with members:
    - "a clever elimination strategy for efficient minimal solvers"
    - "a minimal solution for two view focal length estimation using two affine correspondences"
    - "radially distorted conjugate translations"
    - "graph cut ransac"
    - "mods fast and robust method for two view matching"

Connected through hyperedge HE-5 (induced by source paper "recovering affine features from orientation and scale invariant ones"):
  with members:
    - "a minimal solution for two view focal length estimation using two affine correspondences"
    - "radially distorted conjugate translations"
    - "graph cut ransac"
    - "mods fast and robust method for two view matching"

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)