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: "deep ten texture encoding network"
  Abstract: We propose a Deep Texture Encoding Network (Deep-TEN) with a novel Encoding Layer integrated on top of convolutional layers, which ports the entire dictionary learning and encoding pipeline into a single model. Current methods build from distinct components, using standard encoders with separate off-the-shelf features such as SIFT descriptors or pre-trained CNN features for material recognition. Our new approach provides an end-to-end learning framework, where the inherent visual vocabularies are learned directly from the loss function. The features, dictionaries and the encoding representation for the classifier are all learned simultaneously. The representation is orderless and therefore is particularly useful for material and texture recognition. The Encoding Layer generalizes robust residual encoders such as VLAD and Fisher Vectors, and has the property of discarding domain specific information which makes the learned convolutional features easier to transfer. Additionally, joint training using multiple datasets of varied sizes and class labels is supported resulting in increased recognition performance. The experimental results show superior performance as compared to state-of-the-art methods using gold-standard databases such as MINC-2500, Flickr Material Database, KTH-TIPS-2b, and two recent databases 4D-Light-Field-Material and GTOS. The source code for the complete system are publicly available.

Connected through hyperedge HE-1 (induced by source paper "histogram layers for texture analysis"):
  with members:
    - "learnable histogram statistical context features for deep neural networks"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "deep texture manifold for ground terrain recognition"
    - "texture classification in extreme scale variations using ganet"
    - "a theoretical analysis of deep neural networks for texture classification"
    - "deep residual learning for image recognition"
    - "material recognition in the wild with the materials in context database"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (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"
    - "factorized bilinear models for image recognition"

Connected through hyperedge HE-3 (induced by source paper "fine grained recognition accounting for subtle differences between similar classes"):
  with members:
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "multi attention multi class constraint for fine grained image recognition"
    - "deep texture manifold for ground terrain recognition"
    - "maximum entropy fine grained classification"
    - "learning to navigate for fine grained classification"
    - "rethinking feature distribution for loss functions in image classification"
    - "pairwise confusion for fine grained visual classification"

Connected through hyperedge HE-4 (induced by source paper "material segmentation of multi view satellite imagery"):
  with members:
    - "context encoding for semantic segmentation"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "stacked hourglass networks for human pose estimation"
    - "learning to push by grasping using multiple tasks for effective learning"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "material recognition cnns and hierarchical planning for biped robot locomotion on slippery terrain"

Connected through hyperedge HE-5 (induced by source paper "topology maintained structure encoding"):
  with members:
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "universal sentence encoder"
    - "progressive growing of gans for improved quality stability and variation"
    - "richer convolutional features for edge detection"
    - "improved training of wasserstein gans"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "vote3deep fast object detection in 3d point clouds using efficient convolutional neural networks"
    - "octnet learning deep 3d representations at high resolutions"

Connected through hyperedge HE-6 (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"
    - "using filter banks in convolutional neural networks for texture classification"
    - "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-7 (induced by source paper "multi level texture encoding and representation multer based on deep neural networks"):
  with members:
    - "deep texture manifold for ground terrain recognition"
    - "deep residual learning for image recognition"
    - "material recognition in the wild with the materials in context database"
    - "imagenet large scale visual recognition challenge"
    - "describing textures in the wild"

Connected through hyperedge HE-8 (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"
    - "approximate fisher kernels of non iid image models for image categorization"
    - "using filter banks in convolutional neural networks for texture classification"
    - "densely connected 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)