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: "aognets compositional grammatical architectures for deep learning"
  Abstract: Neural architectures are the foundation for improving performance of deep neural networks (DNNs). This paper presents deep compositional grammatical architectures which harness the best of two worlds: grammar models and DNNs. The proposed architectures integrate compositionality and reconfigurability of the former and the capability of learning rich features of the latter in a principled way. We utilize AND-OR Grammar (AOG) as network generator in this paper and call the resulting networks AOGNets. An AOGNet consists of a number of stages each of which is composed of a number of AOG building blocks. An AOG building block splits its input feature map into N groups along feature channels and then treat it as a sentence of N words. It then jointly realizes a phrase structure grammar and a dependency grammar in bottom-up parsing the "sentence" for better feature exploration and reuse. It provides a unified framework for the best practices developed in state-of-the-art DNNs. In experiments, AOGNet is tested in the CIFAR-10, CIFAR-100 and ImageNet-1K classification benchmark and the MS-COCO object detection and segmentation benchmark. In CIFAR-10, CIFAR-100 and ImageNet-1K, AOGNet obtains better performance than ResNet and most of its variants, ResNeXt and its attention based variants such as SENet, DenseNet and DualPathNet. AOGNet also obtains the best model interpretability score using network dissection. AOGNet further shows better potential in adversarial defense. In MS-COCO, AOGNet obtains better performance than the ResNet and ResNeXt backbones in Mask R-CNN.

Connected through hyperedge HE-1 (induced by source paper "concept whitening for interpretable image recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "the convolutional tsetlin machine"
    - "educe explaining model decisions through unsupervised concepts extraction"
    - "saccader improving accuracy of hard attention models for vision"
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "on concept based explanations in deep neural networks"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "this looks like that deep learning for interpretable image recognition"

Connected through hyperedge HE-2 (induced by source paper "compositional convolutional neural networks a deep architecture with innate robustness to partial occlusion"):
  with members:
    - "combining compositional models and deep networks for robust object classification under occlusion"
    - "compositional convolutional networks for robust object classification under occlusion"
    - "tdapnet prototype network with recurrent top down attention for robust object classification under partial occlusion"
    - "greedy structure learning of hierarchical compositional models"
    - "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"
    - "visual concepts and compositional voting"
    - "interpretable convolutional neural networks"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-3 (induced by source paper "attentive normalization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "instance level meta normalization"
    - "image synthesis from reconfigurable layout and style"
    - "searching for mobilenetv3"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "fixup initialization residual learning without normalization"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"

Connected through hyperedge HE-4 (induced by source paper "gated convolutional networks with hybrid connectivity for image classification"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "selective kernel networks"
    - "log densenet how to sparsify a densenet"
    - "dropblock a regularization method for convolutional networks"
    - "regularized evolution for image classifier architecture search"
    - "mixed link networks"
    - "autoaugment learning augmentation policies from data"

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)