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: "plmp point line minimal problems in complete multi view visibility"
  Abstract: We present a complete classification of all minimal problems for generic arrangements of points and lines completely observed by calibrated perspective cameras. We show that there are only 30 minimal problems in total, no problems exist for more than 6 cameras, for more than 5 points, and for more than 6 lines. We present a sequence of tests for detecting minimality starting with counting degrees of freedom and ending with full symbolic and numeric verification of representative examples. For all minimal problems discovered, we present their algebraic degrees, i.e. the number of solutions, which measure their intrinsic difficulty. It shows how exactly the difficulty of problems grows with the number of views. Importantly, several new minimal problems have small degrees that might be practical in image matching and 3D reconstruction.

Connected through hyperedge HE-1 (induced by source paper "holistically attracted wireframe parsing"):
  with members:
    - "learning regional attraction for line segment detection"
    - "learning to calibrate straight lines for fisheye image rectification"
    - "ppgnet learning point pair graph for line segment detection"
    - "surface reconstruction from 3d line segments"
    - "point based multi view stereo network"
    - "end to end wireframe parsing"
    - "cornernet detecting objects as paired keypoints"
    - "learning attraction field representation for robust line segment detection"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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"
    - "five point fundamental matrix estimation for uncalibrated cameras"
    - "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"

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)