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: "multispecies fruit flower detection using a refined semantic segmentation network"
  Abstract: In fruit production, critical crop management decisions are guided by bloom intensity, i.e., the number of flowers present in an orchard. Despite its importance, bloom intensity is still typically estimated by means of human visual inspection. Existing automated computer vision systems for flower identification are based on hand-engineered techniques that work only under specific conditions and with limited performance. This letter proposes an automated technique for flower identification that is robust to uncontrolled environments and applicable to different flower species. Our method relies on an end-to-end residual convolutional neural network (CNN) that represents the state-of-the-art in semantic segmentation. To enhance its sensitivity to flowers, we fine-tune this network using a single dataset of apple flower images. Since CNNs tend to produce coarse segmentations, we employ a refinement method to better distinguish between individual flower instances. Without any preprocessing or dataset-specific training, experimental results on images of apple, peach, and pear flowers, acquired under different conditions demonstrate the robustness and broad applicability of our method.

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"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "apple flower detection using deep convolutional networks"
    - "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"
    - "fruit quantity and quality estimation using a robotic vision system"
    - "apple flower detection using deep convolutional networks"
    - "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 "the rosario dataset multisensor data for localization and mapping in agricultural environments"):
  with members:
    - "3d soil compaction mapping through kriging based exploration with a mobile robot"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "automatic model based dataset generation for fast and accurate crop and weeds detection"
    - "weednet dense semantic weed classification using multispectral images and mav for smart farming"

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)