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: "deep fruit detection in orchards"
  Abstract: An accurate and reliable image based fruit detection system is critical for supporting higher level agriculture tasks such as yield mapping and robotic harvesting. This paper presents the use of a state-of-the-art object detection framework, Faster R-CNN, in the context of fruit detection in orchards, including mangoes, almonds and apples. Ablation studies are presented to better understand the practical deployment of the detection network, including how much training data is required to capture variability in the dataset. Data augmentation techniques are shown to yield significant performance gains, resulting in a greater than two-fold reduction in the number of training images required. In contrast, transferring knowledge between orchards contributed to negligible performance gain over initialising the Deep Convolutional Neural Network directly from ImageNet features. Finally, to operate over orchard data containing between 100-1000 fruit per image, a tiling approach is introduced for the Faster R-CNN framework. The study has resulted in the best yet detection performance for these orchards relative to previous works, with an F1-score of >0.9 achieved for apples and mangoes.

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"
    - "a comparative study of fruit detection and counting methods for yield mapping in apple orchards"
    - "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"

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 hierarchical grocery store image dataset with visual and semantic labels"):
  with members:
    - "advances in variational inference"
    - "deep variational canonical correlation analysis"
    - "generative models of visually grounded imagination"
    - "fruit recognition from images using deep learning"
    - "densely connected convolutional networks"
    - "inter battery topic representation learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep metric learning via lifted structured feature embedding"

Connected through hyperedge HE-4 (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"
    - "image segmentation for fruit detection and yield estimation in apple orchards"

Connected through hyperedge HE-5 (induced by source paper "modern cnns for iot based farms"):
  with members:
    - "deep learning in agriculture a survey"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "towards principled design of deep convolutional networks introducing simpnet"
    - "an analysis of deep neural network models for practical applications"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "using deep learning for image based plant disease detection"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "fruit detection segmentation and 3d visualisation of environments in apple orchards"):
  with members:
    - "single pixel reconstruction for one stage instance segmentation"
    - "yolov3 an incremental improvement"
    - "deep learning in agriculture a survey"
    - "focal loss for dense object detection"
    - "segicp integrated deep semantic segmentation and pose estimation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "mask r cnn"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-8 (induced by source paper "deep mangoes from fruit detection to cultivar identification in colour images of mango trees"):
  with members:
    - "soft nms improving object detection with one line of code"
    - "an implementation of faster rcnn with study for region sampling"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "mask r cnn"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "segnet a deep convolutional encoder decoder architecture for image 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)