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 robotic vision system to measure tree traits"
  Abstract: The autonomous measurement of tree traits, such as branching structure, branch diameters, branch lengths, and branch angles, is required for tasks such as robotic pruning of trees as well as structural phenotyping. We propose a robotic vision system called the Robotic System for Tree Shape Estimation (RoTSE) to determine tree traits in field settings. The process is composed of the following stages: image acquisition with a mobile robot unit, segmentation, reconstruction, curve skeletonization, conversion to a graph representation, and then computation of traits. Quantitative and qualitative results on apple trees are shown in terms of accuracy, computation time, and robustness. Compared to ground truth measurements, the RoTSE produced the following estimates: branch diameter (mean-squared error 0.99 mm), branch length (mean-squared error 45.64 mm), and branch angle (mean-squared error 10.36 degrees). The average run time was 8.47 minutes when the voxel resolution was 3 mm3.

Connected through hyperedge HE-1 (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"
    - "a novel method for the extrinsic calibration of a 2d laser rangefinder and a camera"
    - "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"
    - "mask r cnn"
    - "image segmentation for fruit detection and yield estimation in apple orchards"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "calibration of asynchronous camera networks for object reconstruction tasks"):
  with members:
    - "tree morphology for phenotyping from semantics based mapping in orchard environments"
    - "geometric loss functions for camera pose regression with deep learning"
    - "dpc net deep pose correction for visual localization"
    - "solving the robot world hand eye s calibration problem with iterative methods"
    - "relative camera pose estimation using convolutional neural networks"
    - "panoptic studio a massively multiview system for social interaction capture"

Connected through hyperedge HE-3 (induced by source paper "the use of agricultural robots in orchard management"):
  with members:
    - "an overview of perception methods for horticultural robots from pollination to harvest"
    - "tree morphology for phenotyping from semantics based mapping in orchard environments"
    - "multispecies fruit flower detection using a refined semantic segmentation network"
    - "fruit quantity and quality estimation using a robotic vision system"
    - "apple flower detection using deep convolutional networks"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "automatic segmentation of trees in dynamic outdoor environments"):
  with members:
    - "apple flower detection using deep convolutional networks"
    - "seeds superpixels extracted via energy driven sampling"

Connected through hyperedge HE-5 (induced by source paper "tree morphology for phenotyping from semantics based mapping in orchard environments"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "a novel method for extrinsic calibration of a 2 d laser rangefinder and a camera"

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.RO(Robotics)