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: "camera pose estimation from lines using plucker coordinates"
  Abstract: Correspondences between 3D lines and their 2D images captured by a camera are often used to determine position and orientation of the camera in space. In this work, we propose a novel algebraic algorithm to estimate the camera pose. We parameterize 3D lines using Pl\"ucker coordinates that allow linear projection of the lines into the image. A line projection matrix is estimated using Linear Least Squares and the camera pose is then extracted from the matrix. An algebraic approach to handle mismatched line correspondences is also included. The proposed algorithm is an order of magnitude faster yet comparably accurate and robust to the state-of-the-art, it does not require initialization, and it yields only one solution. The described method requires at least 9 lines and is particularly suitable for scenarios with 25 and more lines, as also shown in the results.

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"
    - "plmp point line minimal problems in complete multi view visibility"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-2 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency"):
  with members:
    - "low latency visual slam with appearance enhanced local map building"
    - "good feature selection for least squares pose optimization in vo vslam"
    - "navigating the landscape for real time localization and mapping for robotics and virtual and augmented reality"
    - "the open vision computer an integrated sensing and compute system for mobile robots"
    - "direct sparse odometry with rolling shutter"
    - "futuremapping the computational structure of spatial ai systems"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "the tum vi benchmark for evaluating visual inertial odometry"

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)