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 dictionary learning a parametric network approach"
  Abstract: Deep dictionary learning seeks multiple dictionaries at different image scales to capture complementary coherent characteristics. We propose a method for learning a hierarchy of synthesis dictionaries with an image classification goal. The dictionaries and classification parameters are trained by a classification objective, and the sparse features are extracted by reducing a reconstruction loss in each layer. The reconstruction objectives in some sense regularize the classification problem and inject source signal information in the extracted features. The performance of the proposed hierarchical method increases by adding more layers, which consequently makes this model easier to tune and adapt. The proposed algorithm furthermore shows a remarkably lower fooling rate in the presence of adversarial perturbation. The validation of the proposed approach is based on its classification performance using four benchmark datasets and is compared to a Convolutional Neural Network (CNN) of similar size.

Connected through hyperedge HE-1 (induced by source paper "deep transform and metric learning network wedding deep dictionary learning and neural networks"):
  with members:
    - "analysis dictionary learning an efficient and discriminative solution"
    - "analysis dictionary learning based classification structure for robustness"
    - "structured analysis dictionary learning for image classification"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "striving for simplicity the all convolutional net"
    - "playing with duality an overview of recent primal dual approaches for solving large scale optimization problems"

Connected through hyperedge HE-2 (induced by source paper "deep network classification by scattering and homotopy dictionary learning"):
  with members:
    - "scattering networks for hybrid representation learning"
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "kymatio scattering transforms in python"
    - "scaling the scattering transform deep hybrid networks"
    - "supervised deep sparse coding networks"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "deep roto translation scattering for object classification"

Connected through hyperedge HE-3 (induced by source paper "analysis dictionary learning based classification structure for robustness"):
  with members:
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "resfeats residual network based features for image classification"
    - "deep residual learning for image recognition"
    - "visualizing and understanding convolutional networks"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-4 (induced by source paper "peernets exploiting peer wisdom against adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "adversarial attacks and defences competition"
    - "dynamic graph cnn for learning on point clouds"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "non local neural networks"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "video frame interpolation via adaptive convolution"

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)