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: "multiview differential geometry of curves"
  Abstract: The field of multiple view geometry has seen tremendous progress in reconstruction and calibration due to methods for extracting reliable point features and key developments in projective geometry. Point features, however, are not available in certain applications and result in unstructured point cloud reconstructions. General image curves provide a complementary feature when keypoints are scarce, and result in 3D curve geometry, but face challenges not addressed by the usual projective geometry of points and algebraic curves. We address these challenges by laying the theoretical foundations of a framework based on the differential geometry of general curves, including stationary curves, occluding contours, and non-rigid curves, aiming at stereo correspondence, camera estimation (including calibration, pose, and multiview epipolar geometry), and 3D reconstruction given measured image curves. By gathering previous results into a cohesive theory, novel results were made possible, yielding three contributions. First, we derive the differential geometry of an image curve (tangent, curvature, curvature derivative) from that of the underlying space curve (tangent, curvature, curvature derivative, torsion). Second, we derive the differential geometry of a space curve from that of two corresponding image curves. Third, the differential motion of an image curve is derived from camera motion and the differential geometry and motion of the space curve. The availability of such a theory enables novel curve-based multiview reconstruction and camera estimation systems to augment existing point-based approaches. This theory has been used to reconstruct a "3D curve sketch", to determine camera pose from local curve geometry, and tracking; other developments are underway.

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"
    - "five point fundamental matrix estimation for uncalibrated cameras"
    - "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"

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"
    - "five point fundamental matrix estimation for uncalibrated cameras"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "trifocal relative pose from lines at points and its efficient solution"):
  with members:
    - "resultant based incremental recovery of camera pose from pairwise matches"
    - "stereo relative pose from line and point feature triplets"
    - "beyond gr obner bases basis selection for minimal solvers"
    - "joint spatio temporal boundary detection and boundary flow prediction with a fully convolutional siamese network"
    - "a line point unified solution to relative camera pose estimation"
    - "on some properties of calibrated trifocal tensors"
    - "from multiview image curves to 3d drawings"

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)