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: "beyond gr obner bases basis selection for minimal solvers"
  Abstract: Many computer vision applications require robust estimation of the underlying geometry, in terms of camera motion and 3D structure of the scene. These robust methods often rely on running minimal solvers in a RANSAC framework. In this paper we show how we can make polynomial solvers based on the action matrix method faster, by careful selection of the monomial bases. These monomial bases have traditionally been based on a Gr\"obner basis for the polynomial ideal. Here we describe how we can enumerate all such bases in an efficient way. We also show that going beyond Gr\"obner bases leads to more efficient solvers in many cases. We present a novel basis sampling scheme that we evaluate on a number of problems.

Connected through hyperedge HE-1 (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"
    - "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-2 (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"
    - "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"
    - "multiview differential geometry of curves"

Connected through hyperedge HE-3 (induced by source paper "minimal solvers for rectifying from radially distorted scales and change of scales"):
  with members:
    - "repeatability is not enough learning affine regions via discriminability"
    - "radially distorted conjugate translations"
    - "coplanar repeats by energy minimization"
    - "tilt transform invariant low rank textures"

Connected through hyperedge HE-4 (induced by source paper "rectification from radially distorted scales"):
  with members:
    - "repeatability is not enough learning affine regions via discriminability"
    - "radially distorted conjugate translations"
    - "coplanar repeats by energy minimization"
    - "tilt transform invariant low rank textures"

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)