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: "an empirical study of spatial attention mechanisms in deep networks"
  Abstract: Attention mechanisms have become a popular component in deep neural networks, yet there has been little examination of how different influencing factors and methods for computing attention from these factors affect performance. Toward a better general understanding of attention mechanisms, we present an empirical study that ablates various spatial attention elements within a generalized attention formulation, encompassing the dominant Transformer attention as well as the prevalent deformable convolution and dynamic convolution modules. Conducted on a variety of applications, the study yields significant findings about spatial attention in deep networks, some of which run counter to conventional understanding. For example, we find that the query and key content comparison in Transformer attention is negligible for self-attention, but vital for encoder-decoder attention. A proper combination of deformable convolution with key content only saliency achieves the best accuracy-efficiency tradeoff in self-attention. Our results suggest that there exists much room for improvement in the design of attention mechanisms.

Connected through hyperedge HE-1 (induced by source paper "attentive group equivariant convolutional networks"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "general e 2 equivariant steerable cnns"
    - "building deep equivariant capsule networks"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "stand alone self attention in vision models"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "attention augmented convolutional networks"
    - "bam bottleneck attention module"

Connected through hyperedge HE-2 (induced by source paper "attention a lightweight 2d hand pose estimation approach"):
  with members:
    - "real time facial surface geometry from monocular video on mobile gpus"
    - "making convolutional networks shift invariant again"
    - "freihand a dataset for markerless capture of hand pose and shape from single rgb images"
    - "3d hand shape and pose from images in the wild"
    - "attention augmented convolutional networks"
    - "mish a self regularized non monotonic neural activation function"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-3 (induced by source paper "follow the attention combining partial pose and object motion for fine grained action detection"):
  with members:
    - "skeleton based action recognition of people handling objects"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "sample efficient deep reinforcement learning for dialogue systems with large action spaces"
    - "simple baselines for human pose estimation and tracking"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "densepose dense human pose estimation in the wild"

Connected through hyperedge HE-4 (induced by source paper "stand alone self attention in vision models"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "local relation networks for image recognition"
    - "nas fpn learning scalable feature pyramid architecture for object detection"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "self attention with relative position representations"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "mmdetection open mmlab detection toolbox and benchmark"):
  with members:
    - "double head rcnn rethinking classification and localization for object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "region proposal by guided anchoring"
    - "simpledet a simple and versatile distributed framework for object detection and instance recognition"
    - "feature selective anchor free module for single shot object detection"
    - "mask scoring r cnn"

Connected through hyperedge HE-6 (induced by source paper "lip local importance based pooling"):
  with members:
    - "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"
    - "detail preserving pooling in deep networks"
    - "deformable convnets v2 more deformable better results"

Connected through hyperedge HE-7 (induced by source paper "empirical upper bound in object detection and more"):
  with members:
    - "an analysis of pre training on object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "efficientdet scalable and efficient object detection"
    - "contextual relabelling of detected objects"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "mask scoring r cnn"
    - "object detection in 20 years a survey"

Connected through hyperedge HE-8 (induced by source paper "centermask real time anchor free instance segmentation"):
  with members:
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "centernet keypoint triplets for object detection"
    - "an energy and gpu computation efficient backbone network for real time object detection"
    - "mask scoring r cnn"
    - "high resolution representations for labeling pixels and regions"
    - "tensormask a foundation for dense object segmentation"
    - "fcos fully convolutional one stage object detection"
    - "yolact real time instance segmentation"

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)