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 "pointpwc net a coarse to fine network for supervised and self supervised scene flow estimation on 3d point clouds"):
  Abstract: We propose a novel end-to-end deep scene flow model, called PointPWC-Net, on 3D point clouds in a coarse-to-fine fashion. Flow computed at the coarse level is upsampled and warped to a finer level, enabling the algorithm to accommodate for large motion without a prohibitive search space. We introduce novel cost volume, upsampling, and warping layers to efficiently handle 3D point cloud data. Unlike traditional cost volumes that require exhaustively computing all the cost values on a high-dimensional grid, our point-based formulation discretizes the cost volume onto input 3D points, and a PointConv operation efficiently computes convolutions on the cost volume. Experiment results on FlyingThings3D outperform the state-of-the-art by a large margin. We further explore novel self-supervised losses to train our model and achieve comparable results to state-of-the-art trained with supervised loss. Without any fine-tuning, our method also shows great generalization ability on KITTI Scene Flow 2015 dataset, outperforming all previous methods.

Includes members:
  Member "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "motionnet joint perception and motion prediction for autonomous driving based on bird s eye view maps")
  Member "monte carlo convolution for learning on non uniformly sampled point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pointasnl robust point clouds processing using nonlocal neural networks with adaptive sampling")
  Member "pointflownet learning representations for rigid motion estimation from point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "flownet3d geometric losses for deep scene flow estimation")
  Member "flownet3d learning scene flow in 3d point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "df net unsupervised joint learning of depth and flow using cross task consistency":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency")
  Member "splatnet sparse lattice networks for point cloud processing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review deep learning on 3d point clouds")
  Member "geonet unsupervised learning of dense depth optical flow and camera pose":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")
  Member "pixel2mesh generating 3d mesh models from single rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on deep geometry learning from a representation perspective")

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)