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: "factorized bilinear models for image recognition"
  Abstract: Although Deep Convolutional Neural Networks (CNNs) have liberated their power in various computer vision tasks, the most important components of CNN, convolutional layers and fully connected layers, are still limited to linear transformations. In this paper, we propose a novel Factorized Bilinear (FB) layer to model the pairwise feature interactions by considering the quadratic terms in the transformations. Compared with existing methods that tried to incorporate complex non-linearity structures into CNNs, the factorized parameterization makes our FB layer only require a linear increase of parameters and affordable computational cost. To further reduce the risk of overfitting of the FB layer, a specific remedy called DropFactor is devised during the training process. We also analyze the connection between FB layer and some existing models, and show FB layer is a generalization to them. Finally, we validate the effectiveness of FB layer on several widely adopted datasets including CIFAR-10, CIFAR-100 and ImageNet, and demonstrate superior results compared with various state-of-the-art deep models.

Connected through hyperedge HE-1 (induced by source paper "multimodal unified attention networks for vision and language interactions"):
  with members:
    - "shared predictive cross modal deep quantization"
    - "multimodal deep network embedding with integrated structure and attribute information"
    - "deep modular co attention networks for visual question answering"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "dynamic fusion with intra and inter modality attention flow for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "eca net efficient channel attention for deep convolutional neural networks"):
  with members:
    - "expectation maximization attention networks for semantic segmentation"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "attention augmented convolutional networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "ccnet criss cross attention for semantic segmentation"
    - "channelnets compact and efficient convolutional neural networks via channel wise convolutions"
    - "gather excite exploiting feature context in convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "w posenet dense correspondence regularized pixel pair pose regression"):
  with members:
    - "6d pose estimation with correlation fusion"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "grasp pose detection in point clouds"

Connected through hyperedge HE-4 (induced by source paper "deep cnns meet global covariance pooling better representation and generalization"):
  with members:
    - "cbam convolutional block attention module"
    - "is second order information helpful for large scale visual recognition"
    - "squeeze and excitation networks"
    - "improved bilinear pooling with cnns"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "towards effective codebookless model for image classification"
    - "aggregated residual transformations for deep neural networks"
    - "deep ten texture encoding network"

Connected through hyperedge HE-5 (induced by source paper "action recognition with spatial temporal discriminative filter banks"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "multi fiber networks for video recognition"
    - "videos as space time region graphs"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"

Connected through hyperedge HE-6 (induced by source paper "learning deep bilinear transformation for fine grained image representation"):
  with members:
    - "bilinear attention networks"
    - "global second order pooling convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "attention is all you need"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "improved bilinear pooling with cnns"
    - "condensenet an efficient densenet using learned group convolutions"
    - "diversified visual attention networks for fine grained object classification"

Connected through hyperedge HE-7 (induced by source paper "efficient codebook and factorization for second order representation learning"):
  with members:
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "leveraging implicit spatial information in global features for image retrieval"
    - "deep metric learning with hierarchical triplet loss"
    - "sampling matters in deep embedding learning"
    - "no fuss distance metric learning using proxies"
    - "hadamard product for low rank bilinear pooling"
    - "low rank bilinear pooling for fine grained classification"
    - "learning deep embeddings with histogram loss"

Connected through hyperedge HE-8 (induced by source paper "deep fusion an attention guided factorized bilinear pooling for audio video emotion recognition"):
  with members:
    - "attention based fully convolutional network for speech emotion recognition"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"

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)