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: "learn to scale generating multipolar normalized density map for crowd counting"
  Abstract: Dense crowd counting aims to predict thousands of human instances from an image, by calculating integrals of a density map over image pixels. Existing approaches mainly suffer from the extreme density variances. Such density pattern shift poses challenges even for multi-scale model ensembling. In this paper, we propose a simple yet effective approach to tackle this problem. First, a patch-level density map is extracted by a density estimation model and further grouped into several density levels which are determined over full datasets. Second, each patch density map is automatically normalized by an online center learning strategy with a multipolar center loss. Such a design can significantly condense the density distribution into several clusters, and enable that the density variance can be learned by a single model. Extensive experiments demonstrate the superiority of the proposed method. Our work outperforms the state-of-the-art by 4.2%, 14.3%, 27.1% and 20.1% in MAE, on ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF-QNRF datasets, respectively.

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"
    - "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"
    - "a ccnn adaptive ccnn for density estimation and crowd counting"

Connected through hyperedge HE-2 (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"
    - "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"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-3 (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"
    - "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"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"

Connected through hyperedge HE-4 (induced by source paper "using depth for pixel wise detection of adversarial attacks in crowd counting"):
  with members:
    - "detecting the unexpected via image resynthesis"
    - "bayesian loss for crowd count estimation with point supervision"
    - "multi level bottom top and top bottom feature fusion for crowd counting"
    - "point in box out beyond counting persons in crowds"
    - "attacking optical flow"
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "learning spatial awareness to improve crowd counting"
    - "counting with focus for free"

Connected through hyperedge HE-5 (induced by source paper "estimating people flows to better count them in crowded scenes"):
  with members:
    - "locality constrained spatial transformer network for video crowd counting"
    - "bayesian loss for crowd count estimation with point supervision"
    - "multi level bottom top and top bottom feature fusion for crowd counting"
    - "point in box out beyond counting persons in crowds"
    - "learning spatial awareness to improve crowd counting"
    - "counting with focus for free"
    - "perspective guided convolution networks for crowd counting"
    - "learning from synthetic data for crowd counting in the wild"

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)