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 "on the realization and analysis of circular harmonic transforms for feature detection"):
  Abstract: Cartesian-separable realizations of circular-harmonic decompositions for angular spectrum estimation are presented and a powerful test-statistic for rotation-invariant feature-detection in images is proposed. It is shown that pixel-domain realizations of the resulting finite impulse response (FIR) filters have a low computational complexity as a consequence of their separability and steerability. The chosen form also focuses the impulse response around the pixel-under test while ensuring adequate angular resolution after discretization. The novel test-statistic, involving angular integrals that are evaluated in the transform domain, is used to detect wedges, i.e. corners of arbitrary angle and unknown orientation, in synthetic and real imagery. Like traditional (t- and F-distributed) test statistics used in regression analysis, and unlike other rotationally invariant detectors, this similarity measure incorporates and considers uncertainty due to the limitations of finite sampling.

Includes members:
  Member "scale steerable filters for locally scale invariant convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scale equivariant steerable networks")
  Member "gabor convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "discrete approximations of the affine gaussian derivative model for visual receptive fields":
    also connected through other hyperedges:
      (none)
  Member "angular accuracy of steerable feature detectors":
    also connected through other hyperedges:
      (none)

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)