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: "apple flower detection using deep convolutional networks"
  Abstract: Abstract   To optimize fruit production, a portion of the flowers and fruitlets of apple trees must be removed early in the growing season. The proportion to be removed is determined by the bloom intensity, i.e., the number of flowers present in the orchard. Several automated computer vision systems have been proposed to estimate bloom intensity, but their overall performance is still far from satisfactory even in relatively controlled environments. With the goal of devising a technique for flower identification which is robust to clutter and to changes in illumination, this paper presents a method in which a pre-trained convolutional neural network is fine-tuned to become specially sensitive to flowers. Experimental results on a challenging dataset demonstrate that our method significantly outperforms three approaches that represent the state of the art in flower detection, with recall and precision rates higher than 90%. Moreover, a performance assessment on three additional datasets previously unseen by the network, which consist of different flower species and were acquired under different conditions, reveals that the proposed method highly surpasses baseline approaches in terms of generalization capability.

Connected through hyperedge HE-1 (induced by source paper "freelabel a publicly available annotation tool based on freehand traces"):
  with members:
    - "superpixels an evaluation of the state of the art"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "semantic segmentation refinement by monte carlo region growing of high confidence detections"
    - "iteratively trained interactive segmentation"
    - "multispecies fruit flower detection using a refined semantic segmentation network"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "looking at outfit to parse clothing"
    - "mask r cnn"

Connected through hyperedge HE-2 (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"
    - "a robotic vision system to measure tree traits"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "multispecies fruit flower detection using a refined semantic segmentation network"):
  with members:
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "image segmentation for fruit detection and yield estimation in apple orchards"
    - "coco stuff thing and stuff classes in context"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "learning deconvolution network for semantic segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "hypercolumns for object segmentation and fine grained localization"

Connected through hyperedge HE-4 (induced by source paper "automatic segmentation of trees in dynamic outdoor environments"):
  with members:
    - "a robotic vision system to measure tree traits"
    - "seeds superpixels extracted via energy driven sampling"

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)