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: "fruit quantity and quality estimation using a robotic vision system"
  Abstract: Accurate localisation of crop remains highly challenging in unstructured environments such as farms. Many of the developed systems still rely on the use of hand selected features for crop identification and often neglect the estimation of crop quantity and quality, which is key to assigning labor during farming processes. To alleviate these limitations we present a robotic vision system that can accurately estimate the quantity and quality of sweet pepper (Capsicum annuum L), a key horticultural crop. This system consists of three parts: detection, quality estimation, and tracking. Efficient detection is achieved using the FasterRCNN framework. Quality is then estimated in the same framework by learning a parallel layer which we show experimentally results in superior performance than treating quality as extra classes in the traditional Faster-RCNN framework. Evaluation of these two techniques outlines the improved performance of the parallel layer, where we achieve an F1 score of 77.3 for the parallel technique yet only 72.5 for the best scoring (red) of the multi-class implementation. To track the crop we present a tracking via detection approach, which uses the FasterRCNN with parallel layers, that is also a vision-only solution. This approach is cheap to implement as it only requires a camera and in experiments across 2 days we show that our proposed system can accurately estimate the number of sweet pepper present, within 4.1% of the ground truth.

Connected through hyperedge HE-1 (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"
    - "image matching using sift surf brief and orb performance comparison for distorted images"
    - "image segmentation for fruit detection and yield estimation in apple orchards"
    - "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-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"
    - "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 "an overview of perception methods for horticultural robots from pollination to harvest"):
  with members:
    - "from plants to landmarks time invariant plant localization that uses deep pose regression in agricultural fields"
    - "autonomous sweet pepper harvesting for protected cropping systems"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "faster r cnn towards real time object detection with region proposal networks"

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)