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: "single pixel reconstruction for one stage instance segmentation"
  Abstract: Object instance segmentation is one of the most fundamental but challenging tasks in computer vision, and it requires the pixel-level image understanding. Most existing approaches address this problem by adding a mask prediction branch to a two-stage object detector with the Region Proposal Network (RPN). Although producing good segmentation results, the efficiency of these two-stage approaches is far from satisfactory, restricting their applicability in practice. In this paper, we propose a one-stage framework, SPRNet, which performs efficient instance segmentation by introducing a single pixel reconstruction (SPR) branch to off-the-shelf one-stage detectors. The added SPR branch reconstructs the pixel-level mask from every single pixel in the convolution feature map directly. Using the same ResNet-50 backbone, SPRNet achieves comparable mask AP to Mask R-CNN at a higher inference speed, and gains all-round improvements on box AP at every scale comparing with RetinaNet.

Connected through hyperedge HE-1 (induced by source paper "fruit detection segmentation and 3d visualisation of environments in apple orchards"):
  with members:
    - "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"
    - "deep fruit detection in orchards"

Connected through hyperedge HE-2 (induced by source paper "sais single stage anchor free instance segmentation"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "polarmask single shot instance segmentation with polar representation"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "mask scoring r cnn"
    - "tensormask a foundation for dense object segmentation"
    - "fcos fully convolutional one stage object detection"
    - "yolact real time instance segmentation"
    - "bottom up object detection by grouping extreme and center points"

Connected through hyperedge HE-3 (induced by source paper "visual perception and modelling in unstructured orchard for apple harvesting robots"):
  with members:
    - "fruit detection segmentation and 3d visualisation of environments in apple orchards"
    - "fast autonomous flight in warehouses for inventory applications"
    - "yolov3 an incremental improvement"
    - "deep learning in agriculture a survey"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "mask r cnn"
    - "deep fruit detection in orchards"
    - "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)