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 "learning propagation for arbitrarily structured data"):
  Abstract: Processing an input signal that contains arbitrary structures, e.g., superpixels and point clouds, remains a big challenge in computer vision. Linear diffusion, an effective model for image processing, has been recently integrated with deep learning algorithms. In this paper, we propose to learn pairwise relations among data points in a global fashion to improve semantic segmentation with arbitrarily-structured data, through spatial generalized propagation networks (SGPN). The network propagates information on a group of graphs, which represent the arbitrarily-structured data, through a learned, linear diffusion process. The module is flexible to be embedded and jointly trained with many types of networks, e.g., CNNs. We experiment with semantic segmentation networks, where we use our propagation module to jointly train on different data -- images, superpixels and point clouds. We show that SGPN consistently improves the performance of both pixel and point cloud segmentation, compared to networks that do not contain this module. Our method suggests an effective way to model the global pairwise relations for arbitrarily-structured data.

Includes members:
  Member "surfconv bridging 3d and 2d convolution for rgbd images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "leveraging shape completion for 3d siamese tracking")
  Member "depth estimation via affinity learned with convolutional spatial propagation network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "a simple baseline algorithm for graph classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an api oriented open source python framework for unsupervised learning on graphs")
  Member "splatnet sparse lattice networks for point cloud processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review deep learning on 3d point clouds")
  Member "fast end to end trainable guided filter":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "predicting sharp and accurate occlusion boundaries in monocular depth estimation using displacement fields")
  Member "videos as space time region graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "tangent convolutions for dense prediction in 3d":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pointasnl robust point clouds processing using nonlocal neural networks with adaptive sampling")

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)