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: "improving object counting with heatmap regulation"
  Abstract: In this paper, we propose a simple and effective way to improve one-look regression models for object counting from images. We use class activation map visualizations to illustrate the drawbacks of learning a pure one-look regression model for a counting task. Based on these insights, we enhance one-look regression counting models by regulating activation maps from the final convolution layer of the network with coarse ground-truth activation maps generated from simple dot annotations. We call this strategy heatmap regulation (HR). We show that this simple enhancement effectively suppresses false detections generated by the corresponding one-look baseline model and also improves the performance in terms of false negatives. Evaluations are performed on four different counting datasets --- two for car counting (CARPK, PUCPR+), one for crowd counting (WorldExpo) and another for biological cell counting (VGG-Cells). Adding HR to a simple VGG front-end improves performance on all these benchmarks compared to a simple one-look baseline model and results in state-of-the-art performance for car counting.

Connected through hyperedge HE-1 (induced by source paper "object counting with small datasets of large images"):
  with members:
    - "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"
    - "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:
    - "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"
    - "leaf counting with deep convolutional and deconvolutional networks"
    - "mask r cnn"
    - "crowdnet a deep convolutional network for dense crowd counting"

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)