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: "gcnet non local networks meet squeeze excitation networks and beyond"
  Abstract: The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies, via aggregating query-specific global context to each query position. However, through a rigorous empirical analysis, we have found that the global contexts modeled by non-local network are almost the same for different query positions within an image. In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation. We further observe that this simplified design shares similar structure with Squeeze-Excitation Network (SENet). Hence we unify them into a three-step general framework for global context modeling. Within the general framework, we design a better instantiation, called the global context (GC) block, which is lightweight and can effectively model the global context. The lightweight property allows us to apply it for multiple layers in a backbone network to construct a global context network (GCNet), which generally outperforms both simplified NLNet and SENet on major benchmarks for various recognition tasks. The code and configurations are released at this https URL.

Connected through hyperedge HE-1 (induced by source paper "revisiting the sibling head in object detector"):
  with members:
    - "reppoints point set representation for object detection"
    - "double head rcnn rethinking classification and localization for object detection"
    - "centernet keypoint triplets for object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "dropblock a regularization method for convolutional networks"

Connected through hyperedge HE-2 (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"
    - "stand alone self attention in vision models"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "attention augmented convolutional networks"
    - "bam bottleneck attention module"

Connected through hyperedge HE-3 (induced by source paper "channel equilibrium networks for learning deep representation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "switchable whitening for deep representation learning"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "pixel level dense prediction without decoder"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "high resolution representations for labeling pixels and regions"
    - "deep high resolution representation learning for human pose estimation"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-5 (induced by source paper "context gated convolution"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "attention augmented convolutional networks"
    - "generating long sequences with sparse transformers"
    - "weight standardization"
    - "eco efficient convolutional network for online video understanding"
    - "bam bottleneck attention module"
    - "image transformer"

Connected through hyperedge HE-6 (induced by source paper "spatial group wise enhance improving semantic feature learning in convolutional networks"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "searching for mobilenetv3"
    - "selective kernel networks"
    - "weight standardization"
    - "bam bottleneck attention module"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "image super resolution using very deep residual channel attention networks"
    - "lcanet end to end lipreading with cascaded attention ctc"

Connected through hyperedge HE-7 (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"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "region proposal by guided anchoring"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "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-8 (induced by source paper "linear context transform block"):
  with members:
    - "searching for mobilenetv3"
    - "selective kernel networks"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "bam bottleneck attention module"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "group normalization"
    - "learning a discriminative feature network for semantic segmentation"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

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)