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 "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.

Includes members:
  Member "decoupled networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural similarity learning")
  Member "correlation flow robust optical flow using kernel cross correlators":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust target relative localization with ultra wideband ranging and communication")
  Member "kernel cross correlator":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "keyfilter aware real time uav object tracking")
  Member "non linear convolution filters for cnn based learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quadratic autoencoder q ae for low dose ct denoising")
  Member "ultra wideband aided fast localization and mapping system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "uwb based system for uav localization in gnss denied environments characterization and dataset")
  Member "non iterative slam":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intensity scan context coding intensity and geometry relations for loop closure detection")
  Member "splinecnn fast geometric deep learning with continuous b spline kernels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep graph matching consensus")
  Member "dynamic routing between capsules":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lightweight residual densely connected convolutional neural network")

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)