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: "active convolution learning the shape of convolution for image classification"
  Abstract: In recent years, deep learning has achieved great success in many computer vision applications. Convolutional neural networks (CNNs) have lately emerged as a major approach to image classification. Most research on CNNs thus far has focused on developing architectures such as the Inception and residual networks. The convolution layer is the core of the CNN, but few studies have addressed the convolution unit itself. In this paper, we introduce a convolution unit called the active convolution unit (ACU). A new convolution has no fixed shape, because of which we can define any form of convolution. Its shape can be learned through backpropagation during training. Our proposed unit has a few advantages. First, the ACU is a generalization of convolution; it can define not only all conventional convolutions, but also convolutions with fractional pixel coordinates. We can freely change the shape of the convolution, which provides greater freedom to form CNN structures. Second, the shape of the convolution is learned while training and there is no need to tune it by hand. Third, the ACU can learn better than a conventional unit, where we obtained the improvement simply by changing the conventional convolution to an ACU. We tested our proposed method on plain and residual networks, and the results showed significant improvement using our method on various datasets and architectures in comparison with the baseline.

Connected through hyperedge HE-1 (induced by source paper "arma nets expanding receptive field for dense prediction"):
  with members:
    - "scale aware trident networks for object detection"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "deformable convnets v2 more deformable better results"
    - "smoothed dilated convolutions for improved dense prediction"
    - "contextvp fully context aware video prediction"
    - "learning affinity via spatial propagation networks"
    - "deformable convolutional networks"
    - "dilated residual networks"

Connected through hyperedge HE-2 (induced by source paper "spatially adaptive filter units for compact and efficient deep neural networks"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "spatially adaptive filter units for deep neural networks"
    - "deformable convolutional networks"
    - "dilated residual networks"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "neural similarity learning"):
  with members:
    - "compressive hyperspherical energy minimization"
    - "implicit regularization in deep matrix factorization"
    - "a closer look at few shot classification"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "decoupled networks"
    - "meta learning with latent embedding optimization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "towards understanding the role of over parametrization in generalization of neural networks"

Connected through hyperedge HE-4 (induced by source paper "fisheyedistancenet self supervised scale aware distance estimation using monocular fisheye camera for autonomous driving"):
  with members:
    - "woodscape a multi task multi camera fisheye dataset for autonomous driving"
    - "3d packing for self supervised monocular depth estimation"
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "rethinking imagenet pre training"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "group normalization"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "artistic style transfer for videos and spherical images"

Connected through hyperedge HE-5 (induced by source paper "selective kernel networks"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "bam bottleneck attention module"
    - "residual networks of residual networks multilevel residual networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "image super resolution using very deep residual channel attention networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "person search via a mask guided two stream cnn model"
    - "igcv 2 interleaved structured sparse convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "pod practical object detection with scale sensitive network"):
  with members:
    - "rethinking imagenet pre training"
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "acquisition of localization confidence for accurate object detection"
    - "improving object localization with fitness nms and bounded iou loss"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "cascade r cnn delving into high quality object detection"

Connected through hyperedge HE-7 (induced by source paper "efficient segmentation learning downsampling near semantic boundaries"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "beyond gradient descent for regularized segmentation losses"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformable convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "interaction and aggregation network for person re identification"):
  with members:
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "harmonious attention network for person re identification"
    - "cbam convolutional block attention module"
    - "resource aware person re identification across multiple resolutions"
    - "non local neural networks"
    - "scalable person re identification on supervised smoothed manifold"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"

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)