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: "leveraging multiple datasets for deep leaf counting"
  Abstract: The number of leaves a plant has is one of the key traits (phenotypes) describing its development and growth. Here, we propose an automated, deep learning based approach for counting leaves in model rosette plants. While state-of-the-art results on leaf counting with deep learning methods have recently been reported, they obtain the count as a result of leaf segmentation and thus require per-leaf (instance) segmentation to train the models (a rather strong annotation). Instead, our method treats leaf counting as a direct regression problem and thus only requires as annotation the total leaf count per plant. We argue that combining different datasets when training a deep neural network is beneficial and improves the results of the proposed approach. We evaluate our method on the CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. Experimental results show that the proposed method significantly outperforms the winner of the previous CVPPP challenge, improving the results by a minimum of ~50% on each of the test datasets, and can achieve this performance without knowing the experimental origin of the data (i.e. in the wild setting of the challenge). We also compare the counting accuracy of our model with that of per leaf segmentation algorithms, achieving a 20% decrease in mean absolute difference in count (|DiC|).

Connected through hyperedge HE-1 (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"
    - "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"
    - "leaf counting with deep convolutional and deconvolutional networks"
    - "mask r cnn"

Connected through hyperedge HE-2 (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"
    - "deepwheat estimating phenotypic traits from crop images with deep learning"
    - "drone based object counting by spatially regularized regional proposal network"
    - "leaf counting with deep convolutional and deconvolutional networks"
    - "mask r cnn"
    - "crowdnet a deep convolutional network for dense crowd counting"

Connected through hyperedge HE-3 (induced by source paper "improving object counting with heatmap regulation"):
  with members:
    - "count ception counting by fully convolutional redundant 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"
    - "leaf counting with deep convolutional and deconvolutional networks"
    - "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-4 (induced by source paper "a pyramid cnn for dense leaves segmentation"):
  with members:
    - "leaf counting with deep convolutional and deconvolutional networks"
    - "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"

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)