human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "robust gabor networks"):
  Abstract: This work takes a step towards investigating the benefits of merging classical vision techniques with deep learning models. Formally, we explore the effect of replacing the first layers of neural network architectures with convolutional layers that are based on Gabor filters with learnable parameters. As a first result, we observe that architectures utilizing Gabor filters as low-level kernels are capable of preserving test set accuracy of deep convolutional networks. Therefore, this architectural change exalts their capabilities in extracting useful low-level features. Furthermore, we observe that the architectures enhanced with Gabor layers gain advantages in terms of robustness when compared to the regular models. Additionally, the existence of a closed mathematical expression for the Gabor kernels allows us to develop an analytical expression for an upper bound to the Lipschitz constant of the Gabor layer. This expression allows us to propose a simple regularizer to enhance the robustness of the network. We conduct extensive experiments with several architectures and datasets, and show the beneficial effects that the introduction of Gabor layers has on the robustness of deep convolutional networks.

Includes members:
  Member "adversarial sensor attack on lidar based perception in autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ghostimage perception domain attacks against vision based object classification systems")
  Member "kervolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph optimization approach to range based localization")
  Member "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ghostimage perception domain attacks against vision based object classification systems")
  Member "the singular values of convolutional layers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding generalization in deep learning via tensor methods")
  Member "robustness via curvature regularization and vice versa":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "varmixup exploiting the latent space for robust training and inference")
  Member "structured adversarial attack towards general implementation and better interpretability":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "varmixup exploiting the latent space for robust training and inference")
  Member "gabor filter assisted energy efficient fast learning convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "filter bank regularization of convolutional neural networks")
  Member "deep watershed transform for instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")

Question: Please tell me which class the source paper 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)