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: "integrating deep features for material recognition"
  Abstract: We propose a method for integration of features extracted using deep representations of Convolutional Neural Networks (CNNs) each of which is learned using a different image dataset of objects and materials for material recognition. Given a set of representations of multiple pre-trained CNNs, we first compute activations of features using the representations on the images to select a set of samples which are best represented by the features. Then, we measure the uncertainty of the features by computing the entropy of class distributions for each sample set. Finally, we compute the contribution of each feature to representation of classes for feature selection and integration. We examine the proposed method on three benchmark datasets for material recognition. Experimental results show that the proposed method achieves state-of-the-art performance by integrating deep features. Additionally, we introduce a new material dataset called EFMD by extending Flickr Material Database (FMD). By the employment of the EFMD with transfer learning for updating the learned CNN models, we achieve 84.0%+/-1.8% accuracy on the FMD dataset which is close to human performance that is 84.9%.

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 "evaluating artificial systems for pairwise ranking tasks sensitive to individual differences"):
  with members:
    - "image aesthetic assessment an experimental survey"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "second order democratic aggregation"):
  with members:
    - "a deeper look at power normalizations"
    - "statistically motivated second order pooling"
    - "is second order information helpful for large scale visual recognition"
    - "improved bilinear pooling with cnns"
    - "second order convolutional neural networks"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "interferences in match kernels"
    - "compact bilinear pooling"

Connected through hyperedge HE-4 (induced by source paper "a deeper look at power normalizations"):
  with members:
    - "improved bilinear pooling with cnns"
    - "domain adaptation by mixture of alignments of second or higher order scatter tensors"
    - "a riemannian network for spd matrix learning"
    - "bilinear cnn models for fine grained visual recognition"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"
    - "places205 vggnet models for scene recognition"

Connected through hyperedge HE-5 (induced by source paper "material recognition from local appearance in global context"):
  with members:
    - "discovering perceptual attributes in a deep local material recognition network"
    - "deeply recursive convolutional network for image super resolution"
    - "human pose estimation with iterative error feedback"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

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)