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: "image segmentation for fruit detection and yield estimation in apple orchards"
  Abstract: Ground vehicles equipped with monocular vision systems are a valuable source of high resolution image data for precision agriculture applications in orchards. This paper presents an image processing framework for fruit detection and counting using orchard image data. A general purpose image segmentation approach is used, including two feature learning algorithms; multi-scale Multi-Layered Perceptrons (MLP) and Convolutional Neural Networks (CNN). These networks were extended by including contextual information about how the image data was captured (metadata), which correlates with some of the appearance variations and/or class distributions observed in the data. The pixel-wise fruit segmentation output is processed using the Watershed Segmentation (WS) and Circular Hough Transform (CHT) algorithms to detect and count individual fruits. Experiments were conducted in a commercial apple orchard near Melbourne, Australia. The results show an improvement in fruit segmentation performance with the inclusion of metadata on the previously benchmarked MLP network. We extend this work with CNNs, bringing agrovision closer to the state-of-the-art in computer vision, where although metadata had negligible influence, the best pixel-wise F1-score of $0.791$ was achieved. The WS algorithm produced the best apple detection and counting results, with a detection F1-score of $0.858$. As a final step, image fruit counts were accumulated over multiple rows at the orchard and compared against the post-harvest fruit counts that were obtained from a grading and counting machine. The count estimates using CNN and WS resulted in the best performance for this dataset, with a squared correlation coefficient of $r^2=0.826$.

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"
    - "a robotic vision system to measure tree traits"
    - "mask r cnn"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "grape detection segmentation and tracking using deep neural networks and three dimensional association"):
  with members:
    - "monocular camera based fruit counting and mapping with semantic data association"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "deep learning for generic object detection a survey"
    - "deep learning in agriculture a survey"
    - "datasheets for datasets"
    - "agricultural robotics the future of robotic agriculture"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "a comparative study of fruit detection and counting methods for yield mapping in apple orchards"):
  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"
    - "focal loss for dense object detection"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "feature pyramid networks for object detection"
    - "deep fruit detection in orchards"

Connected through hyperedge HE-4 (induced by source paper "a novel weight shared multi stage cnn for scale robustness"):
  with members:
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"
    - "deep learning in neural networks"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (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"
    - "vision based preharvest yield mapping for apple orchards"
    - "fruit quantity and quality estimation using a robotic vision system"
    - "image matching using sift surf brief and orb performance comparison for distorted images"
    - "deep fruit detection in orchards"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "multispecies fruit flower detection using a refined semantic segmentation network"):
  with members:
    - "apple flower detection using deep convolutional networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "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-7 (induced by source paper "apple flower detection using deep convolutional networks"):
  with members:
    - "superpixels an evaluation of the state of the art"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "understanding data augmentation for classification when to warp"
    - "deep fruit detection in orchards"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "how transferable are features in deep neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "robust fruit counting combining deep learning tracking and structure from motion"):
  with members:
    - "deep fruit detection in orchards"
    - "very deep convolutional networks for large scale image recognition"
    - "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.RO(Robotics)