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: "kervolutional neural networks"
  Abstract: Convolutional neural networks (CNNs) have enabled the state-of-the-art performance in many computer vision tasks. However, little effort has been devoted to establishing convolution in non-linear space. Existing works mainly leverage on the activation layers, which can only provide point-wise non-linearity. To solve this problem, a new operation, kervolution (kernel convolution), is introduced to approximate complex behaviors of human perception systems leveraging on the kernel trick. It generalizes convolution, enhances the model capacity, and captures higher order interactions of features, via patch-wise kernel functions, but without introducing additional parameters. Extensive experiments show that kervolutional neural networks (KNN) achieve higher accuracy and faster convergence than baseline CNN.

Connected through hyperedge HE-1 (induced by source paper "graph optimization approach to range based localization"):
  with members:
    - "kernel cross correlator"
    - "ultra wideband aided fast localization and mapping system"
    - "non iterative slam"
    - "machine learning in wireless sensor networks algorithms strategies and applications"

Connected through hyperedge HE-2 (induced by source paper "robust gabor networks"):
  with members:
    - "adversarial sensor attack on lidar based perception in autonomous driving"
    - "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction"
    - "the singular values of convolutional layers"
    - "robustness via curvature regularization and vice versa"
    - "structured adversarial attack towards general implementation and better interpretability"
    - "gabor filter assisted energy efficient fast learning convolutional neural networks"
    - "deep watershed transform for instance segmentation"
    - "wide residual networks"

Connected through hyperedge HE-3 (induced by source paper "learning the non linearity in convolutional neural networks"):
  with members:
    - "neural arithmetic logic units"
    - "convolutional neural networks with recurrent neural filters"
    - "non linear convolution filters for cnn based learning"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

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)