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 unlabeled data for crowd counting by learning to rank"
  Abstract: We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework. To induce a ranking of cropped images , we use the observation that any sub-image of a crowded scene image is guaranteed to contain the same number or fewer persons than the super-image. This allows us to address the problem of limited size of existing datasets for crowd counting. We collect two crowd scene datasets from Google using keyword searches and query-by-example image retrieval, respectively. We demonstrate how to efficiently learn from these unlabeled datasets by incorporating learning-to-rank in a multi-task network which simultaneously ranks images and estimates crowd density maps. Experiments on two of the most challenging crowd counting datasets show that our approach obtains state-of-the-art results.

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 "nas count counting by density with neural architecture search"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "detnas backbone search for object detection"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "nas bench 101 towards reproducible neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"

Connected through hyperedge HE-3 (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"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"

Connected through hyperedge HE-4 (induced by source paper "rethinking object detection in retail stores"):
  with members:
    - "reppoints point set representation for object detection"
    - "data priming network for automatic check out"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "rpc a large scale retail product checkout dataset"
    - "object counting and instance segmentation with image level supervision"
    - "cornernet detecting objects as paired keypoints"
    - "mvtec d2s densely segmented supermarket dataset"

Connected through hyperedge HE-5 (induced by source paper "focus on semantic consistency for cross domain crowd understanding"):
  with members:
    - "scar spatial channel wise attention regression networks for crowd counting"
    - "c 3 framework an open source pytorch code for crowd counting"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "learning from synthetic data for crowd counting in the wild"
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "fast video crowd counting with a temporal aware network"):
  with members:
    - "scar spatial channel wise attention regression networks for crowd counting"
    - "learning spatial awareness to improve crowd counting"
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "learning from synthetic data for crowd counting in the wild"
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "attend to count crowd counting with adaptive capacity multi scale cnns"
    - "crowd counting with deep structured scale integration network"
    - "composition loss for counting density map estimation and localization in dense crowds"

Connected through hyperedge HE-7 (induced by source paper "hybrid graph neural networks for crowd counting"):
  with members:
    - "dense scale network for crowd counting"
    - "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"
    - "revisiting perspective information for efficient crowd counting"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "learning human object interactions by graph parsing neural networks"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-8 (induced by source paper "crowd counting via hierarchical scale recalibration network"):
  with members:
    - "enhanced 3d convolutional networks for crowd counting"
    - "crowd counting on images with scale variation and isolated clusters"
    - "improving the learning of multi column convolutional neural network for crowd counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "attend to count crowd counting with adaptive capacity multi scale cnns"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "crowd counting by adaptively fusing predictions from an image pyramid"

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)