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: "a novel method for the extrinsic calibration of a 2d laser rangefinder and a camera"
  Abstract: We present a novel method for extrinsically calibrating a camera and a 2D laser rangefinder whose beams are invisible from the camera image. We show that the point-to-plane constraints from a single observation of a V-shaped calibration pattern composed of two non-coplanar triangles suffice to uniquely constrain the relative pose between two sensors. Next, we present an approach to obtain analytical solutions using point-to-plane constraints from single or multiple observations. Along the way, we also show that the previous solutions, in contrast to our method, have inherent ambiguities and therefore must rely on a good initial estimate. Real and synthetic experiments validate our method and show that it achieves better accuracy than the previous methods.

Connected through hyperedge HE-1 (induced by source paper "ellipse r cnn learning to infer elliptical object from clustering and occlusion"):
  with members:
    - "semantic mapping for orchard environments by merging two sides reconstructions of tree rows"
    - "minneapple a benchmark dataset for apple detection and segmentation"
    - "a comparative study of fruit detection and counting methods for yield mapping in apple orchards"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"
    - "network dissection quantifying interpretability of deep visual representations"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "squeeze and excitation networks"

Connected through hyperedge HE-2 (induced by source paper "semantic mapping for orchard environments by merging two sides reconstructions of tree rows"):
  with members:
    - "a comparative study of fruit detection and counting methods for yield mapping in apple orchards"
    - "tree morphology for phenotyping from semantics based mapping in orchard environments"
    - "vision based preharvest yield mapping for apple orchards"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "a robotic vision system to measure tree traits"
    - "mask r cnn"
    - "image segmentation for fruit detection and yield estimation in apple orchards"
    - "deep residual learning for image recognition"

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)