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: "iterative crowd counting"
  Abstract: In this work, we tackle the problem of crowd counting in images. We present a Convolutional Neural Network (CNN) based density estimation approach to solve this problem. Predicting a high resolution density map in one go is a challenging task. Hence, we present a two branch CNN architecture for generating high resolution density maps, where the first branch generates a low resolution density map, and the second branch incorporates the low resolution prediction and feature maps from the first branch to generate a high resolution density map. We also propose a multi-stage extension of our approach where each stage in the pipeline utilizes the predictions from all the previous stages. Empirical comparison with the previous state-of-the-art crowd counting methods shows that our method achieves the lowest mean absolute error on three challenging crowd counting benchmarks: Shanghaitech, WorldExpo'10, and UCF datasets.

Connected through hyperedge HE-1 (induced by source paper "ha ccn hierarchical attention based crowd counting network"):
  with members:
    - "inverse attention guided deep crowd counting network"
    - "crowd counting and density estimation by trellis encoder decoder network"
    - "few example object detection with model communication"
    - "mvx net multimodal voxelnet for 3d object detection"
    - "dafe fd density aware feature enrichment for face detection"
    - "learning from synthetic data for crowd counting in the wild"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "pdanet pyramid density aware attention net for accurate crowd counting"):
  with members:
    - "multi scale attention network for crowd counting"
    - "learn to scale generating multipolar normalized density map for crowd counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "denet a universal network for counting crowd with varying densities and scales"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "crowd counting using deep recurrent spatial aware network"
    - "context aware crowd counting"
    - "adaptive scenario discovery for crowd counting"

Connected through hyperedge HE-3 (induced by source paper "nwpu crowd a large scale benchmark for crowd counting"):
  with members:
    - "scar spatial channel wise attention regression networks for crowd counting"
    - "drone based joint density map estimation localization and tracking with space time multi scale attention network"
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "locality constrained spatial transformer network for video crowd counting"
    - "bayesian loss for crowd count estimation with point supervision"
    - "c 3 framework an open source pytorch code for crowd counting"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "perspective guided convolution networks for crowd counting"

Connected through hyperedge HE-4 (induced by source paper "towards using count level weak supervision for crowd counting"):
  with members:
    - "locality constrained spatial transformer network for video crowd counting"
    - "bayesian loss for crowd count estimation with point supervision"
    - "dual path multi scale fusion networks with attention for crowd counting"
    - "enhanced 3d convolutional networks for crowd counting"
    - "feature aware adaptation and structured density alignment for crowd counting in video surveillance"
    - "w net reinforced u net for density map estimation"
    - "dynamic region division for adaptive learning pedestrian counting"
    - "learning from synthetic data for crowd counting in the wild"

Connected through hyperedge HE-5 (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-6 (induced by source paper "3d crowd counting via multi view fusion with 3d gaussian kernels"):
  with members:
    - "learnable triangulation of human pose"
    - "learning from synthetic data for crowd counting in the wild"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "context aware crowd counting"
    - "deepmvs learning multi view stereopsis"
    - "revisiting perspective information for efficient crowd counting"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "crowd counting by adaptively fusing predictions from an image pyramid"

Connected through hyperedge HE-7 (induced by source paper "zoomcount a zooming mechanism for crowd counting in static images"):
  with members:
    - "plug and play rescaling based crowd counting in static images"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "object detection with convolutional neural networks"
    - "mdfn multi scale deep feature learning network for object detection"
    - "towards learning affine invariant representations via data efficient cnns"
    - "boosting occluded image classification via subspace decomposition based estimation of deep features"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "leveraging unlabeled data for crowd counting by learning to rank"

Connected through hyperedge HE-8 (induced by source paper "plug and play rescaling based crowd counting in static images"):
  with members:
    - "zoomcount a zooming mechanism for crowd counting in static images"
    - "counting with focus for free"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "learn to scale generating multipolar normalized density map for crowd counting"
    - "object detection with convolutional neural networks"
    - "mdfn multi scale deep feature learning network for object detection"
    - "boosting occluded image classification via subspace decomposition based estimation of deep features"
    - "from open set to closed set counting objects by spatial divide and conquer"

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)