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: "visualizing and understanding deep texture representations"
  Abstract: A number of recent approaches have used deep convolutional neural networks (CNNs) to build texture representations. Nevertheless, it is still unclear how these models represent texture and invariances to categorical variations. This work conducts a systematic evaluation of recent CNN-based texture descriptors for recognition and attempts to understand the nature of invariances captured by these representations. First we show that the recently proposed bilinear CNN model [25] is an excellent general-purpose texture descriptor and compares favorably to other CNN-based descriptors on various texture and scene recognition benchmarks. The model is translationally invariant and obtains better accuracy on the ImageNet dataset without requiring spatial jittering of data compared to corresponding models trained with spatial jittering. Based on recent work [13, 28] we propose a technique to visualize pre-images, providing a means for understanding categorical properties that are captured by these representations. Finally, we show preliminary results on how a unified parametric model of texture analysis and synthesis can be used for attribute-based image manipulation, e.g. to make an image more swirly, honeycombed, or knitted. The source code and additional visualizations are available at this http URL

Connected through hyperedge HE-1 (induced by source paper "do cnns encode data augmentations"):
  with members:
    - "how much position information do convolutional neural networks encode"
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial examples improve image recognition"
    - "mixmatch a holistic approach to semi supervised learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "self training with noisy student improves imagenet classification"
    - "regularized evolution for image classifier architecture search"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-2 (induced by source paper "visualizing and describing fine grained categories as textures"):
  with members:
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "visualizing deep convolutional neural networks using natural pre images"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "describing textures in the wild"

Connected through hyperedge HE-3 (induced by source paper "do better imagenet models transfer better"):
  with members:
    - "a closer look at few shot classification"
    - "object part attention model for fine grained image classification"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "taskonomy disentangling task transfer learning"
    - "autoaugment learning augmentation policies from data"
    - "exploring the limits of weakly supervised pretraining"
    - "three mechanisms of weight decay regularization"

Connected through hyperedge HE-4 (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"
    - "using filter banks in convolutional neural networks for texture classification"

Connected through hyperedge HE-5 (induced by source paper "generalized orderless pooling performs implicit salient matching"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "weakly supervised fine grained categorization with part based image representation"
    - "interferences in match kernels"
    - "visual concept recognition and localization via iterative introspection"
    - "understanding neural networks through deep visualization"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"

Connected through hyperedge HE-6 (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"
    - "using filter banks in convolutional neural networks for texture classification"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "bam the behance artistic media dataset for recognition beyond photography"):
  with members:
    - "domain adaptation for visual applications a comprehensive survey"
    - "sketch based manga retrieval using manga109 dataset"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "ssd single shot multibox detector"
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"
    - "the amazing mysteries of the gutter drawing inferences between panels in comic book narratives"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-8 (induced by source paper "improved bilinear pooling with cnns"):
  with members:
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "fine grained visual classification of aircraft"

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)