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: "condconv conditionally parameterized convolutions for efficient inference"
  Abstract: Convolutional layers are one of the basic building blocks of modern deep neural networks. One fundamental assumption is that convolutional kernels should be shared for all examples in a dataset. We propose conditionally parameterized convolutions (CondConv), which learn specialized convolutional kernels for each example. Replacing normal convolutions with CondConv enables us to increase the size and capacity of a network, while maintaining efficient inference. We demonstrate that scaling networks with CondConv improves the performance and inference cost trade-off of several existing convolutional neural network architectures on both classification and detection tasks. On ImageNet classification, our CondConv approach applied to EfficientNet-B0 achieves state-of-the-art performance of 78.3% accuracy with only 413M multiply-adds. Code and checkpoints for the CondConv Tensorflow layer and CondConv-EfficientNet models are available at: this https URL.

Connected through hyperedge HE-1 (induced by source paper "revisiting spatial invariance with low rank local connectivity"):
  with members:
    - "stand alone self attention in vision models"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "learn to pay attention"
    - "cbam convolutional block attention module"
    - "you look twice gaternet for dynamic filter selection in cnns"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-2 (induced by source paper "conditional convolutions for instance segmentation"):
  with members:
    - "path aggregation network for instance segmentation"
    - "focal loss for dense object detection"
    - "film visual reasoning with a general conditioning layer"
    - "semantic instance segmentation via deep metric learning"
    - "instance sensitive fully convolutional networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-3 (induced by source paper "domain aware dynamic networks"):
  with members:
    - "test time training for out of distribution generalization"
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "multi source domain adaptation for semantic segmentation"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "searching for mobilenetv3"
    - "single path one shot neural architecture search with uniform sampling"
    - "tafe net task aware feature embeddings for low shot learning"

Connected through hyperedge HE-4 (induced by source paper "deformable kernels adapting effective receptive fields for object deformation"):
  with members:
    - "deformable filter convolution for point cloud reasoning"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "making convolutional networks shift invariant again"
    - "learning by abstraction the neural state machine"
    - "dynamic scale inference by entropy minimization"
    - "kpconv flexible and deformable convolution for point clouds"
    - "selective kernel networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

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)