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: "count ception counting by fully convolutional redundant counting"
  Abstract: Counting objects in digital images is a process that should be replaced by machines. This tedious task is time consuming and prone to errors due to fatigue of human annotators. The goal is to have a system that takes as input an image and returns a count of the objects inside and justification for the prediction in the form of object localization. We repose a problem, originally posed by Lempitsky and Zisserman, to instead predict a count map which contains redundant counts based on the receptive field of a smaller regression network. The regression network predicts a count of the objects that exist inside this frame. By processing the image in a fully convolutional way each pixel is going to be accounted for some number of times, the number of windows which include it, which is the size of each window, (i.e., 32x32 = 1024). To recover the true count we take the average over the redundant predictions. Our contribution is redundant counting instead of predicting a density map in order to average over errors. We also propose a novel deep neural network architecture adapted from the Inception family of networks called the Count-ception network. Together our approach results in a 20% relative improvement (2.9 to 2.3 MAE) over the state of the art method by Xie, Noble, and Zisserman in 2016.

Connected through hyperedge HE-1 (induced by source paper "from open set to closed set supervised spatial divide and conquer for object counting"):
  with members:
    - "bayesian loss for crowd count estimation with point supervision"
    - "learning spatial awareness to improve crowd counting"
    - "learn to scale generating multipolar normalized density map for crowd counting"
    - "pushing the frontiers of unconstrained crowd counting new dataset and benchmark method"
    - "from open set to closed set counting objects by spatial divide and conquer"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "crowd counting using deep recurrent spatial aware network"

Connected through hyperedge HE-2 (induced by source paper "bayesian loss for crowd count estimation with point supervision"):
  with members:
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "where are the blobs counting by localization with point supervision"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "iterative crowd counting"
    - "an aggregated multicolumn dilated convolution network for perspective free counting"
    - "switching convolutional neural network for crowd counting"

Connected through hyperedge HE-3 (induced by source paper "from open set to closed set counting objects by spatial divide and conquer"):
  with members:
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "deep attention based classification network for robust depth prediction"
    - "where are the blobs counting by localization with point supervision"
    - "deep ordinal regression network for monocular depth estimation"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative crowd counting"
    - "switching convolutional neural network for crowd counting"

Connected through hyperedge HE-4 (induced by source paper "histonet predicting size histograms of object instances"):
  with members:
    - "counting the uncountable deep semantic density estimation from space"
    - "mask r cnn"
    - "deep watershed transform for instance segmentation"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "recurrent instance segmentation"

Connected through hyperedge HE-5 (induced by source paper "enhanced center coding for cell detection with convolutional neural networks"):
  with members:
    - "cell detection with deep convolutional neural network and compressed sensing"
    - "mask r cnn"
    - "yolo9000 better faster stronger"
    - "deep watershed transform for instance segmentation"
    - "you only look once unified real time object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "detection of single grapevine berries in images using fully convolutional neural networks"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "class agnostic counting"
    - "mask r cnn"
    - "automated image analysis framework for high throughput determination of grapevine berry sizes using conditional random fields"

Connected through hyperedge HE-7 (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"
    - "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-8 (induced by source paper "where are the blobs counting by localization with point supervision"):
  with members:
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "understanding traffic density from large scale web camera data"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"
    - "a survey of recent advances in cnn based single image crowd counting and density estimation"
    - "mixture of counting cnns adaptive integration of cnns specialized to specific appearance for crowd counting"
    - "ssd single shot multibox detector"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"

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)