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: "crowd counting by adaptively fusing predictions from an image pyramid"
  Abstract: Because of the powerful learning capability of deep neural networks, counting performance via density map estimation has improved significantly during the past several years. However, it is still very challenging due to severe occlusion, large scale variations, and perspective distortion. Scale variations (from image to image) coupled with perspective distortion (within one image) result in huge scale changes of the object size. Earlier methods based on convolutional neural networks (CNN) typically did not handle this scale variation explicitly, until Hydra-CNN and MCNN. MCNN uses three columns, each with different filter sizes, to extract features at different scales. In this paper, in contrast to using filters of different sizes, we utilize an image pyramid to deal with scale variations. It is more effective and efficient to resize the input fed into the network, as compared to using larger filter sizes. Secondly, we adaptively fuse the predictions from different scales (using adaptively changing per-pixel weights), which makes our method adapt to scale changes within an image. The adaptive fusing is achieved by generating an across-scale attention map, which softly selects a suitable scale for each pixel, followed by a 1x1 convolution. Extensive experiments on three popular datasets show very compelling results.

Connected through hyperedge HE-1 (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-2 (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"
    - "iterative crowd counting"

Connected through hyperedge HE-3 (induced by source paper "crowd scene analysis by output encoding"):
  with members:
    - "point in box out beyond counting persons in crowds"
    - "centernet keypoint triplets for object detection"
    - "selective kernel networks"
    - "scale aware trident networks for object detection"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "crowd counting using deep recurrent spatial aware network"
    - "where are the blobs counting by localization with point supervision"
    - "top down feedback for crowd counting convolutional neural network"

Connected through hyperedge HE-4 (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"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"

Connected through hyperedge HE-5 (induced by source paper "autoscale learning to scale for crowd counting"):
  with members:
    - "ha ccn hierarchical attention based crowd counting network"
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "point in box out beyond counting persons in crowds"
    - "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"
    - "autofocus efficient multi scale inference"
    - "where are the blobs counting by localization with point supervision"

Connected through hyperedge HE-6 (induced by source paper "baod budget aware object detection"):
  with members:
    - "a flexible model for training action localization with varying levels of supervision"
    - "pcn part and context information for pedestrian detection with cnns"
    - "localization aware active learning for object detection"
    - "towards practical visual search engine within elasticsearch"
    - "generative adversarial active learning"
    - "learning active learning from data"
    - "extreme clicking for efficient object annotation"
    - "deep self taught learning for weakly supervised object localization"

Connected through hyperedge HE-7 (induced by source paper "multi scale attention network for crowd counting"):
  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"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "detecting heads using feature refine net and cascaded multi scale architecture"
    - "pyramid attention network for semantic segmentation"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"
    - "generating high quality crowd density maps using contextual pyramid cnns"

Connected through hyperedge HE-8 (induced by source paper "counting with focus for free"):
  with members:
    - "crowd counting using scale aware attention networks"
    - "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"
    - "where are the blobs counting by localization with point supervision"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative 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)