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 comparative study of fruit detection and counting methods for yield mapping in apple orchards"
  Abstract: We present new methods for apple detection and counting based on recent deep learning approaches and compare them with state-of-the-art results based on classical methods. Our goal is to quantify performance improvements by neural network-based methods compared to methods based on classical approaches. Additionally, we introduce a complete system for counting apples in an entire row. This task is challenging as it requires tracking fruits in images from both sides of the row. We evaluate the performances of three fruit detection methods and two fruit counting methods on six datasets. Results indicate that the classical detection approach still outperforms the deep learning based methods in the majority of the datasets. For fruit counting though, the deep learning based approach performs better for all of the datasets. Combining the classical detection method together with the neural network based counting approach, we achieve remarkable yield accuracies ranging from 95.56% to 97.83%.

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"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "a novel method for the extrinsic calibration of a 2d laser rangefinder and a camera"
    - "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 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"
    - "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-3 (induced by source paper "minneapple a benchmark dataset for apple detection and segmentation"):
  with members:
    - "robust fruit counting combining deep learning tracking and structure from motion"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "vision based preharvest yield mapping for apple orchards"
    - "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"

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)