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: "leaf counting with deep convolutional and deconvolutional networks"
  Abstract: In this paper, we investigate the problem of counting rosette leaves from an RGB image, an important task in plant phenotyping. We propose a data-driven approach for this task generalized over different plant species and imaging setups. To accomplish this task, we use state-of-the-art deep learning architectures: a deconvolutional network for initial segmentation and a convolutional network for leaf counting. Evaluation is performed on the leaf counting challenge dataset at CVPPP-2017. Despite the small number of training samples in this dataset, as compared to typical deep learning image sets, we obtain satisfactory performance on segmenting leaves from the background as a whole and counting the number of leaves using simple data augmentation strategies. Comparative analysis is provided against methods evaluated on the previous competition datasets. Our framework achieves mean and standard deviation of absolute count difference of 1.62 and 2.30 averaged over all five test datasets.

Connected through hyperedge HE-1 (induced by source paper "segmentation guided attention network for crowd counting via curriculum learning"):
  with members:
    - "ha ccn hierarchical attention based crowd counting network"
    - "inverse attention guided deep crowd counting network"
    - "bayesian loss for crowd count estimation with point supervision"
    - "multi level bottom top and top bottom feature fusion for crowd counting"
    - "point in box out beyond counting persons in crowds"
    - "learning spatial awareness to improve crowd counting"
    - "counting with focus for free"
    - "improving the learning of multi column convolutional neural network for crowd counting"

Connected through hyperedge HE-2 (induced by source paper "object counting with small datasets of large images"):
  with members:
    - "improving object counting with heatmap regulation"
    - "switching convolutional neural network for crowd counting"
    - "count ception counting by fully convolutional redundant counting"
    - "leveraging multiple datasets for deep leaf counting"
    - "deepwheat estimating phenotypic traits from crop images with deep learning"
    - "a survey of recent advances in cnn based single image crowd counting and density estimation"
    - "drone based object counting by spatially regularized regional proposal network"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "semantic binary segmentation using convolutional networks without decoders"):
  with members:
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "rethinking atrous convolution for semantic image segmentation"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "pyramid scene parsing network"
    - "coco stuff thing and stuff classes in context"

Connected through hyperedge HE-4 (induced by source paper "global sum pooling a generalization trick for object counting with small datasets of large images"):
  with members:
    - "improving object counting with heatmap regulation"
    - "switching convolutional neural network for crowd counting"
    - "count ception counting by fully convolutional redundant counting"
    - "leveraging multiple datasets for deep leaf counting"
    - "deepwheat estimating phenotypic traits from crop images with deep learning"
    - "drone based object counting by spatially regularized regional proposal network"
    - "mask r cnn"
    - "crowdnet a deep convolutional network for dense crowd counting"

Connected through hyperedge HE-5 (induced by source paper "improving object counting with heatmap regulation"):
  with members:
    - "count ception counting by fully convolutional redundant counting"
    - "leveraging multiple datasets for deep leaf counting"
    - "a survey of recent advances in cnn based single image crowd counting and density estimation"
    - "drone based object counting by spatially regularized regional proposal network"
    - "mask r cnn"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "end to end instance segmentation with recurrent attention"
    - "fast r cnn"

Connected through hyperedge HE-6 (induced by source paper "a pyramid cnn for dense leaves segmentation"):
  with members:
    - "leveraging multiple datasets for deep leaf counting"
    - "stacked hourglass networks for human pose estimation"
    - "holistically nested edge detection"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"
    - "fast edge detection using structured forests"

Connected through hyperedge HE-7 (induced by source paper "deepwheat estimating phenotypic traits from crop images with deep learning"):
  with members:
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "learning deconvolution network for semantic segmentation"
    - "rethinking the inception architecture for computer vision"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

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)