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: "padnet pan density crowd counting"
  Abstract: The problem of counting crowds in varying density scenes or in different density regions of the same scene, named as pan-density crowd counting, is highly challenging. Previous methods are designed for single density scenes or do not fully utilize pan-density information. We propose a novel framework, the Pan-Density Network (PaDNet), for pan-density crowd counting. In order to effectively capture pan-density information, PaDNet has a novel module, the Density-Aware Network (DAN), that contains multiple sub-networks pretrained on scenarios with different densities. Further, a module named the Feature Enhancement Layer (FEL) is proposed to aggregate the feature maps learned by DAN. It learns an enhancement rate or a weight for each feature map to boost these feature maps. Further, we propose two refined metrics, Patch MAE (PMAE) and Patch RMSE (PRMSE), for better evaluating the model performance on pan-density scenarios. Extensive experiments on four crowd counting benchmark datasets indicate that PaDNet achieves state-of-the-art recognition performance and high robustness in pan-density crowd counting.

Connected through hyperedge HE-1 (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-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"
    - "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-3 (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"
    - "top down feedback for crowd counting convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "learning spatial awareness to improve crowd counting"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "improving the learning of multi column convolutional neural network for 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"
    - "adaptive scenario discovery for crowd counting"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "adversarial complementary learning for weakly supervised object localization"

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)