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 via scale adaptive convolutional neural network"
  Abstract: The task of crowd counting is to automatically estimate the pedestrian number in crowd images. To cope with the scale and perspective changes that commonly exist in crowd images, state-of-the-art approaches employ multi-column CNN architectures to regress density maps of crowd images. Multiple columns have different receptive fields corresponding to pedestrians (heads) of different scales. We instead propose a scale-adaptive CNN (SaCNN) architecture with a backbone of fixed small receptive fields. We extract feature maps from multiple layers and adapt them to have the same output size; we combine them to produce the final density map. The number of people is computed by integrating the density map. We also introduce a relative count loss along with the density map loss to improve the network generalization on crowd scenes with few pedestrians, where most representative approaches perform poorly on. We conduct extensive experiments on the ShanghaiTech, UCF_CC_50 and WorldExpo datasets as well as a new dataset SmartCity that we collect for crowd scenes with few people. The results demonstrate significant improvements of SaCNN over the state-of-the-art.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "drone based rgbt vehicle detection and counting a challenge"):
  with members:
    - "nwpu crowd a large scale benchmark for crowd counting"
    - "locality constrained spatial transformer network for video crowd counting"
    - "perspective guided convolution networks for crowd counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "mrcnet crowd counting and density map estimation in aerial and ground imagery"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "vision meets drones a challenge"
    - "the unmanned aerial vehicle benchmark object detection and tracking"

Connected through hyperedge HE-4 (induced by source paper "a real time deep network for crowd counting"):
  with members:
    - "fast video crowd counting with a temporal aware network"
    - "video crowd counting via dynamic temporal modeling"
    - "adaptive scenario discovery for crowd counting"
    - "top down feedback for crowd counting convolutional neural network"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "switching convolutional neural network for crowd counting"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "cnn based cascaded multi task learning of high level prior and density estimation for crowd counting"

Connected through hyperedge HE-5 (induced by source paper "multi stream networks and ground truth generation for crowd counting"):
  with members:
    - "learning from synthetic data for crowd counting in the wild"
    - "context aware crowd counting"
    - "switching convolutional neural network for crowd counting"
    - "cnn based cascaded multi task learning of high level prior and density estimation for crowd counting"
    - "a survey of recent advances in cnn based single image crowd counting and density estimation"
    - "crowdnet a deep convolutional network for dense crowd counting"
    - "finding tiny faces"

Connected through hyperedge HE-6 (induced by source paper "point in box out beyond counting persons in crowds"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "where are the blobs counting by localization with point supervision"
    - "zigzag learning for weakly supervised object detection"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "revisiting perspective information for efficient crowd counting"
    - "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-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"
    - "crowd counting by adaptively fusing predictions from an image pyramid"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "improving the learning of multi column convolutional neural network for crowd counting"):
  with members:
    - "learning spatial awareness to improve crowd counting"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "stacked pooling improving crowd counting by boosting scale invariance"
    - "in defense of single column networks for crowd counting"
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "context aware crowd counting"
    - "adaptive scenario discovery for crowd counting"
    - "padnet pan density 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)