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: "vision based preharvest yield mapping for apple orchards"
  Abstract: We present an end-to-end computer vision system for mapping yield in an apple orchard using images captured from a single camera. Our proposed system is platform independent and does not require any specific lighting conditions. Our main technical contributions are 1)~a semi-supervised clustering algorithm that utilizes colors to identify apples and 2)~an unsupervised clustering method that utilizes spatial properties to estimate fruit counts from apple clusters having arbitrarily complex geometry. Additionally, we utilize camera motion to merge the counts across multiple views. We verified the performance of our algorithms by conducting multiple field trials on three tree rows consisting of $252$ trees at the University of Minnesota Horticultural Research Center. Results indicate that the detection method achieves $F_1$-measure $.95 -.97$ for multiple color varieties and lighting conditions. The counting method achieves an accuracy of $89\%-98\%$. Additionally, we report merged fruit counts from both sides of the tree rows. Our yield estimation method achieves an overall accuracy of $91.98\% - 94.81\%$ across different datasets.

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "minneapple a benchmark dataset for apple detection and segmentation"):
  with members:
    - "a comparative study of fruit detection and counting methods for yield mapping in apple orchards"
    - "robust fruit counting combining deep learning tracking and structure from motion"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "focal loss for dense object detection"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "feature pyramid networks for object detection"
    - "deep fruit detection in orchards"

Connected through hyperedge HE-3 (induced by source paper "monocular camera based fruit counting and mapping with semantic data association"):
  with members:
    - "semantic mapping for orchard environments by merging two sides reconstructions of tree rows"
    - "robust fruit counting combining deep learning tracking and structure from motion"
    - "fruit quantity and quality estimation using a robotic vision system"
    - "image matching using sift surf brief and orb performance comparison for distorted images"
    - "image segmentation for fruit detection and yield estimation in apple orchards"
    - "deep fruit detection in orchards"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "fully convolutional networks for semantic segmentation"

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)