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: "using filter banks in convolutional neural networks for texture classification"
  Abstract: Deep learning has established many new state of the art solutions in the last decade in areas such as object, scene and speech recognition. In particular Convolutional Neural Network (CNN) is a category of deep learning which obtains excellent results in object detection and recognition tasks. Its architecture is indeed well suited to object analysis by learning and classifying complex (deep) features that represent parts of an object or the object itself. However, some of its features are very similar to texture analysis methods. CNN layers can be thought of as filter banks of complexity increasing with the depth. Filter banks are powerful tools to extract texture features and have been widely used in texture analysis. In this paper we develop a simple network architecture named Texture CNN (T-CNN) which explores this observation. It is built on the idea that the overall shape information extracted by the fully connected layers of a classic CNN is of minor importance in texture analysis. Therefore, we pool an energy measure from the last convolution layer which we connect to a fully connected layer. We show that our approach can improve the performance of a network while greatly reducing the memory usage and computation.

Connected through hyperedge HE-1 (induced by source paper "local rotation invariance in 3d cnns"):
  with members:
    - "b spline cnns on lie groups"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "rotdcf decomposition of convolutional filters for rotation equivariant deep networks"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "rotational 3d texture classification using group equivariant cnns"
    - "cubenet equivariance to 3d rotation and translation"
    - "a general theory of equivariant cnns on homogeneous spaces"
    - "learning steerable filters for rotation equivariant cnns"

Connected through hyperedge HE-2 (induced by source paper "meta learning convolutional neural architectures for multi target concrete defect classification with the concrete defect bridge image dataset"):
  with members:
    - "darts differentiable architecture search"
    - "large scale evolution of image classifiers"
    - "efficient architecture search by network transformation"
    - "wide residual networks"
    - "neural architecture search with reinforcement learning"
    - "designing neural network architectures using reinforcement learning"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "deep adaptive wavelet network"):
  with members:
    - "deep learning in the wavelet domain"
    - "wavelet convolutional neural networks"
    - "scaling the scattering transform deep hybrid networks"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "improving interpretability of deep neural networks with semantic information"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "diversity in fashion recommendation using semantic parsing"):
  with members:
    - "clothing retrieval with visual attention model"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "learning from noisy large scale datasets with minimal supervision"
    - "diversified visual attention networks for fine grained object classification"
    - "deep ten texture encoding network"
    - "learning visual clothing style with heterogeneous dyadic co occurrences"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "spatial transformer networks"

Connected through hyperedge HE-5 (induced by source paper "texture cnn for histopathological image classification"):
  with members:
    - "histopathologic image processing a review"
    - "double transfer learning for breast cancer histopathologic image classification"
    - "rethinking the inception architecture for computer vision"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "texture cnn for thermoelectric metal pipe image classification"):
  with members:
    - "texture cnn for histopathological image classification"

Connected through hyperedge HE-7 (induced by source paper "from bow to cnn two decades of texture representation for texture classification"):
  with members:
    - "deep learning for generic object detection a survey"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "low rank bilinear pooling for fine grained classification"
    - "deep ten texture encoding network"
    - "approximate fisher kernels of non iid image models for image categorization"
    - "densely connected convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "convolutional neural network on three orthogonal planes for dynamic texture classification"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "texture segmentation with fully convolutional networks"
    - "learning rotation invariant convolutional filters for texture classification"
    - "a theoretical analysis of deep neural networks for texture classification"
    - "dynamic texture and scene classification by transferring deep image features"
    - "understanding neural networks through deep visualization"
    - "deep filter banks for texture recognition description and segmentation"
    - "bilinear cnn models for fine grained visual recognition"

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)