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: "detail preserving pooling in deep networks"
  Abstract: Most convolutional neural networks use some method for gradually downscaling the size of the hidden layers. This is commonly referred to as pooling, and is applied to reduce the number of parameters, improve invariance to certain distortions, and increase the receptive field size. Since pooling by nature is a lossy process, it is crucial that each such layer maintains the portion of the activations that is most important for the network's discriminability. Yet, simple maximization or averaging over blocks, max or average pooling, or plain downsampling in the form of strided convolutions are the standard. In this paper, we aim to leverage recent results on image downscaling for the purposes of deep learning. Inspired by the human visual system, which focuses on local spatial changes, we propose detail-preserving pooling (DPP), an adaptive pooling method that magnifies spatial changes and preserves important structural detail. Importantly, its parameters can be learned jointly with the rest of the network. We analyze some of its theoretical properties and show its empirical benefits on several datasets and networks, where DPP consistently outperforms previous pooling approaches.

Connected through hyperedge HE-1 (induced by source paper "lip local importance based pooling"):
  with members:
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "scale aware trident networks for object detection"
    - "bag of tricks for image classification with convolutional neural networks"
    - "detnet a backbone network for object detection"
    - "grid r cnn"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "deformable convnets v2 more deformable better results"

Connected through hyperedge HE-2 (induced by source paper "a closed form learned pooling for deep classification networks"):
  with members:
    - "making convolutional networks shift invariant again"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "polar transformer networks"
    - "learnable pooling with context gating for video classification"
    - "learning bag of features pooling for deep convolutional neural networks"
    - "mask r cnn"
    - "inductive bias of deep convolutional networks through pooling geometry"

Connected through hyperedge HE-3 (induced by source paper "universal pooling a new pooling method for convolutional neural networks"):
  with members:
    - "enhancing operation of a sewage pumping station for inter catchment wastewater transfer by using deep learning and hydraulic model"
    - "cbam convolutional block attention module"
    - "densely connected convolutional networks"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "s3pool pooling with stochastic spatial sampling"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "hartley spectral pooling for deep learning"):
  with members:
    - "s3pool pooling with stochastic spatial sampling"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "striving for simplicity the all convolutional net"
    - "fractional max pooling"

Connected through hyperedge HE-5 (induced by source paper "a survey of model compression and acceleration for deep neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "rethinking the value of network pruning"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "learning efficient convolutional networks through network slimming"
    - "data driven sparse structure selection for deep neural networks"
    - "blockdrop dynamic inference paths in residual networks"

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)