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: "stacked pooling improving crowd counting by boosting scale invariance"
  Abstract: In this work, we explore the cross-scale similarity in crowd counting scenario, in which the regions of different scales often exhibit high visual similarity. This feature is universal both within an image and across different images, indicating the importance of scale invariance of a crowd counting model. Motivated by this, in this paper we propose simple but effective variants of pooling module, i.e., multi-kernel pooling and stacked pooling, to boost the scale invariance of convolutional neural networks (CNNs), benefiting much the crowd density estimation and counting. Specifically, the multi-kernel pooling comprises of pooling kernels with multiple receptive fields to capture the responses at multi-scale local ranges. The stacked pooling is an equivalent form of multi-kernel pooling, while, it reduces considerable computing cost. Our proposed pooling modules do not introduce extra parameters into model and can easily take place of the vanilla pooling layer in implementation. In empirical study on two benchmark crowd counting datasets, the stacked pooling beats the vanilla pooling layer in most cases.

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

Connected through hyperedge HE-2 (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"
    - "in defense of single column networks for crowd counting"
    - "adaptive scenario discovery for crowd counting"
    - "padnet pan density crowd counting"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "adversarial complementary learning for weakly supervised object localization"

Connected through hyperedge HE-3 (induced by source paper "drone based joint density map estimation localization and tracking with space time multi scale attention network"):
  with members:
    - "locality constrained spatial transformer network for video crowd counting"
    - "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"
    - "where are the blobs counting by localization with point supervision"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "deformable convnets v2 more deformable better results"
    - "iterative crowd counting"

Connected through hyperedge HE-4 (induced by source paper "a robust iris authentication system on gpu based edge devices using multi modalities learning model"):
  with members:
    - "deep learning on mobile devices a review"
    - "on the convergence of adam and beyond"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "decoupled weight decay regularization"
    - "tensorlayer a versatile library for efficient deep learning development"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "an overview of gradient descent optimization algorithms"
    - "cyclical learning rates for training neural networks"

Connected through hyperedge HE-5 (induced by source paper "mask aware networks for crowd counting"):
  with members:
    - "top down feedback for crowd counting convolutional neural network"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative crowd counting"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "switching convolutional neural network for crowd counting"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "focal loss for dense object detection"
    - "a survey of recent advances in cnn based single image crowd counting and density estimation"

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)