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 filter banks for texture recognition description and segmentation"
  Abstract: Visual textures have played a key role in image understanding because they convey important semantics of images, and because texture representations that pool local image descriptors in an orderless manner have had a tremendous impact in diverse applications. In this paper we make several contributions to texture understanding. First, instead of focusing on texture instance and material category recognition, we propose a human-interpretable vocabulary of texture attributes to describe common texture patterns, complemented by a new describable texture dataset for benchmarking. Second, we look at the problem of recognizing materials and texture attributes in realistic imaging conditions, including when textures appear in clutter, developing corresponding benchmarks on top of the recently proposed OpenSurfaces dataset. Third, we revisit classic texture representations, including bag-of-visual-words and the Fisher vectors, in the context of deep learning and show that these have excellent efficiency and generalization properties if the convolutional layers of a deep model are used as filter banks. We obtain in this manner state-of-the-art performance in numerous datasets well beyond textures, an efficient method to apply deep features to image regions, as well as benefit in transferring features from one domain to another.

Connected through hyperedge HE-1 (induced by source paper "srm a style based recalibration module for convolutional neural networks"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "a style based generator architecture for generative adversarial networks"
    - "gather excite exploiting feature context in convolutional neural networks"
    - "cbam convolutional block attention module"
    - "non local neural networks"
    - "deep hashing network for unsupervised domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "semantic fisher scores for task transfer using objects to classify scenes"):
  with members:
    - "locally supervised deep hybrid model for scene recognition"
    - "compositional model based fisher vector coding for image classification"
    - "label embedding for image classification"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"
    - "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 "texture retrieval in the wild through detection based attributes"):
  with members:
    - "texel att representing and classifying element based textures by attributes"
    - "a survey of recent advances in texture representation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "visual search at ebay"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "mask r cnn"
    - "whittlesearch interactive image search with relative attribute feedback"

Connected through hyperedge HE-4 (induced by source paper "spatio spectral networks for color texture analysis"):
  with members:
    - "from bow to cnn two decades of texture representation for texture classification"
    - "multilayer complex network descriptors for color texture characterization"
    - "fusion of complex networks and randomized neural networks for texture analysis"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "the tcga meta dataset clinical benchmark"):
  with members:
    - "the omniglot challenge a 3 year progress report"
    - "torchmeta a meta learning library for pytorch"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"
    - "microsoft coco common objects in context"
    - "fine grained visual classification of aircraft"

Connected through hyperedge HE-6 (induced by source paper "brain mediated transfer learning of convolutional neural networks"):
  with members:
    - "using human brain activity to guide machine learning"
    - "soundnet learning sound representations from unlabeled video"
    - "very deep convolutional networks for large scale image recognition"
    - "deeppose human pose estimation via deep neural networks"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "distributed representations of words and phrases and their compositionality"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "texel att representing and classifying element based textures by attributes"):
  with members:
    - "a survey of recent advances in texture representation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "mask r cnn"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "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"
    - "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"

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)