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 convolutional filter banks for texture recognition and segmentation"
  Abstract: Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications. In this work we conduct a first study of material and describable texture at- tributes recognition in clutter, using a new dataset derived from the OpenSurface texture repository. Motivated by the challenge posed by this problem, we propose a new texture descriptor, D-CNN, obtained by Fisher Vector pooling of a Convolutional Neural Network (CNN) filter bank. D-CNN substantially improves the state-of-the-art in texture, mate- rial and scene recognition. Our approach achieves 82.3% accuracy on Flickr material dataset and 81.1% accuracy on MIT indoor scenes, providing absolute gains of more than 10% over existing approaches. D-CNN easily trans- fers across domains without requiring feature adaptation as for methods that build on the fully-connected layers of CNNs. Furthermore, D-CNN can seamlessly incorporate multi-scale information and describe regions of arbitrary shapes and sizes. Our approach is particularly suited at lo- calizing stuff categories and obtains state-of-the-art re- sults on MSRC segmentation dataset, as well as promising results on recognizing materials and surface attributes in clutter on the OpenSurfaces dataset.

Connected through hyperedge HE-1 (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"
    - "using filter banks in convolutional neural networks for texture classification"
    - "wide residual networks"
    - "neural architecture search with reinforcement learning"
    - "designing neural network architectures using reinforcement learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "geometric operator convolutional neural network"):
  with members:
    - "gabor filter assisted energy efficient fast learning convolutional neural networks"
    - "non local neural networks"
    - "deformable convolutional networks"
    - "sgdr stochastic gradient descent with restarts"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"
    - "convolutional neural network architectures for matching natural language sentences"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "universal representations the missing link between faces text planktons and cat breeds"):
  with members:
    - "instance normalization the missing ingredient for fast stylization"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "identity mappings in deep residual networks"
    - "ssd single shot multibox detector"
    - "learning without forgetting"
    - "synthetic data for text localisation in natural images"
    - "instance aware semantic segmentation via multi task network cascades"
    - "simultaneous deep transfer across domains and tasks"

Connected through hyperedge HE-4 (induced by source paper "hybrid cnn and dictionary based models for scene recognition and domain adaptation"):
  with members:
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "learning transferable features with deep adaptation networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "places205 vggnet models for scene recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "how transferable are features in deep neural networks"
    - "deepid net deformable deep convolutional neural networks for object detection"
    - "going deeper with convolutions"

Connected through hyperedge HE-5 (induced by source paper "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"):
  with members:
    - "dense correspondences across scenes and scales"
    - "fully trainable deep matching"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "learning dense correspondence via 3d guided cycle consistency"
    - "vqa visual question answering"
    - "dense semantic correspondence where every pixel is a classifier"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "deep residual learning for image recognition"

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 "backpropagation training for fisher vectors within neural networks"):
  with members:
    - "fisher vectors derived from hybrid gaussian laplacian mixture models for image annotation"
    - "fisher kernel for deep neural activations"

Connected through hyperedge HE-8 (induced by source paper "compositional model based fisher vector coding for image classification"):
  with members:
    - "approximate fisher kernels of non iid image models for image categorization"
    - "bilinear cnn models for fine grained visual recognition"
    - "mid level deep pattern mining"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "encoding high dimensional local features by sparse coding based fisher vectors"
    - "part based r cnns for fine grained category detection"

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)