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: "eca net efficient channel attention for deep convolutional neural networks"
  Abstract: Channel attention has recently demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs). However, most existing methods dedicate to developing more sophisticated attention modules to achieve better performance, inevitably increasing the computational burden. To overcome the paradox of performance and complexity trade-off, this paper makes an attempt to investigate an extremely lightweight attention module for boosting the performance of deep CNNs. In particular, we propose an Efficient Channel Attention (ECA) module, which only involves $k (k < 9)$ parameters but brings clear performance gain. By revisiting the channel attention module in SENet, we empirically show avoiding dimensionality reduction and appropriate cross-channel interaction are important to learn effective channel attention. Therefore, we propose a local cross-channel interaction strategy without dimension reduction, which can be efficiently implemented by a fast 1D convolution. Furthermore, we develop a function of channel dimension to adaptively determine kernel size of 1D convolution, which stands for coverage of local cross-channel interaction. Our ECA module can be flexibly incorporated into existing CNN architectures, and the resulting CNNs are named by ECA-Net. We extensively evaluate the proposed ECA-Net on image classification, object detection and instance segmentation with backbones of ResNets and MobileNetV2. The experimental results show our ECA-Net is more efficient while performing favorably against its counterparts. The source code and models can be available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images"):
  with members:
    - "interlaced sparse self attention for semantic segmentation"
    - "acfnet attentional class feature network for semantic segmentation"
    - "asymmetric non local neural networks for semantic segmentation"
    - "object contextual representations for semantic segmentation"
    - "a relation augmented fully convolutional network for semantic segmentation in aerial scenes"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"

Connected through hyperedge HE-2 (induced by source paper "exemplar normalization for learning deep representation"):
  with members:
    - "channel equilibrium networks for learning deep representation"
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "switchable whitening for deep representation learning"
    - "differentiable learning to group channels via groupable convolutional neural networks"
    - "attentive normalization"
    - "positional normalization"
    - "global second order pooling convolutional networks"
    - "batch kalman normalization towards training deep neural networks with micro batches"

Connected through hyperedge HE-3 (induced by source paper "lossless attention in convolutional networks for facial expression recognition in the wild"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "region attention networks for pose and occlusion robust facial expression recognition"
    - "frame attention networks for facial expression recognition in videos"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "covariance pooling for facial expression recognition"
    - "aff wild2 extending the aff wild database for affect recognition"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised lesion co segmentation on ct scans"):
  with members:
    - "weakly supervised lesion segmentation on ct scans using co segmentation"
    - "xlsor a robust and accurate lung segmentor on chest x rays using criss cross attention and customized radiorealistic abnormalities generation"
    - "abnormal chest x ray identification with generative adversarial one class classifier"
    - "uldor a universal lesion detector for ct scans with pseudo masks and hard negative example mining"
    - "mulan multitask universal lesion analysis network for joint lesion detection tagging and segmentation"
    - "semantic aware attention based deep object co segmentation"
    - "ct image enhancement using stacked generative adversarial networks and transfer learning for lesion segmentation improvement"
    - "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"

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)