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: "non linear convolution filters for cnn based learning"
  Abstract: During the last years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in image classification. Their architectures have largely drawn inspiration by models of the primate visual system. However, while recent research results of neuroscience prove the existence of non-linear operations in the response of complex visual cells, little effort has been devoted to extend the convolution technique to non-linear forms. Typical convolutional layers are linear systems, hence their expressiveness is limited. To overcome this, various non-linearities have been used as activation functions inside CNNs, while also many pooling strategies have been applied. We address the issue of developing a convolution method in the context of a computational model of the visual cortex, exploring quadratic forms through the Volterra kernels. Such forms, constituting a more rich function space, are used as approximations of the response profile of visual cells. Our proposed second-order convolution is tested on CIFAR-10 and CIFAR-100. We show that a network which combines linear and non-linear filters in its convolutional layers, can outperform networks that use standard linear filters with the same architecture, yielding results competitive with the state-of-the-art on these datasets.

Connected through hyperedge HE-1 (induced by source paper "kervolutional neural networks"):
  with members:
    - "decoupled networks"
    - "correlation flow robust optical flow using kernel cross correlators"
    - "kernel cross correlator"
    - "ultra wideband aided fast localization and mapping system"
    - "non iterative slam"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "dynamic routing between capsules"
    - "deformable convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "quadratic autoencoder q ae for low dose ct denoising"):
  with members:
    - "competitive performance of a modularized deep neural network compared to commercial algorithms for low dose ct image reconstruction"
    - "structurally sensitive multi scale deep neural network for low dose ct denoising"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "universal approximation with quadratic deep networks"
    - "fuzzy logic interpretation of artificial neural networks"
    - "dense associative memory is robust to adversarial inputs"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "a new type of neurons for machine learning"

Connected through hyperedge HE-3 (induced by source paper "conquering the cnn over parameterization dilemma a volterra filtering approach for action recognition"):
  with members:
    - "human action recognition and prediction a survey"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "image to image translation with conditional adversarial networks"
    - "convolutional two stream network fusion for video action recognition"
    - "deep temporal linear encoding networks"
    - "tensorflow a system for large scale machine learning"
    - "bilinear cnns for fine grained visual recognition"

Connected through hyperedge HE-4 (induced by source paper "learning the non linearity in convolutional neural networks"):
  with members:
    - "kervolutional neural networks"
    - "neural arithmetic logic units"
    - "convolutional neural networks with recurrent neural filters"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "a push pull layer improves robustness of convolutional neural networks"):
  with members:
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "learning steerable filters for rotation equivariant cnns"
    - "harmonic networks deep translation and rotation equivariance"
    - "wide residual networks"
    - "improving the robustness of deep neural networks via stability training"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-6 (induced by source paper "global second order pooling convolutional networks"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "dropblock a regularization method for convolutional networks"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "multi scale location aware kernel representation for object detection"
    - "gather excite exploiting feature context in convolutional neural networks"
    - "cbam convolutional block attention module"
    - "non local neural networks"
    - "attention is all you need"

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)