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 "lrf net learning local reference frames for 3d local shape description and matching"):
  Abstract: The local reference frame (LRF) acts as a critical role in 3D local shape description and matching. However, most of existing LRFs are hand-crafted and suffer from limited repeatability and robustness. This paper presents the first attempt to learn an LRF via a Siamese network that needs weak supervision only. In particular, we argue that each neighboring point in the local surface gives a unique contribution to LRF construction and measure such contributions via learned weights. Extensive analysis and comparative experiments on three public datasets addressing different application scenarios have demonstrated that LRF-Net is more repeatable and robust than several state-of-the-art LRF methods (LRF-Net is only trained on one dataset). In addition, LRF-Net can significantly boost the local shape description and 6-DoF pose estimation performance when matching 3D point clouds.

Includes members:
  Member "3d local features for direct pairwise registration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "end to end learning local multi view descriptors for 3d point clouds")
  Member "learning an effective equivariant 3d descriptor without supervision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quaternion equivariant capsule networks for 3d point clouds")
  Member "the perfect match 3d point cloud matching with smoothed densities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "end to end learning local multi view descriptors for 3d point clouds")
  Member "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a rotation invariant framework for deep point cloud analysis")
  Member "rotational projection statistics for 3d local surface description and object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mgcn descriptor learning using multiscale gcns")

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)