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 "just go with the flow self supervised scene flow estimation"):
  Abstract: When interacting with highly dynamic environments, scene flow allows autonomous systems to reason about the non-rigid motion of multiple independent objects. This is of particular interest in the field of autonomous driving, in which many cars, people, bicycles, and other objects need to be accurately tracked. Current state of the art methods require annotated scene flow data from autonomous driving scenes to train scene flow networks with supervised learning. As an alternative, we present a method of training scene flow that uses two self-supervised losses, based on nearest neighbors and cycle consistency. These self-supervised losses allow us to train our method on large unlabeled autonomous driving datasets; the resulting method matches current state-of-the-art supervised performance using no real world annotations and exceeds state-of-the-art performance when combining our self-supervised approach with supervised learning on a smaller labeled dataset.

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 "learning correspondence from the cycle consistency of time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "nuscenes a multimodal dataset for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review deep learning on 3d point clouds")
  Member "unsupervised multi task feature learning on point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "memory based graph networks")
  Member "deep lidar cnn to understand the dynamics of moving vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving map re localization with deep movable objects segmentation on 3d lidar point clouds")
  Member "pointflownet learning representations for rigid motion estimation from point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pointpwc net a coarse to fine network for supervised and self supervised scene flow estimation on 3d point clouds")
  Member "flownet3d learning scene flow in 3d point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "pointnet deep hierarchical feature learning on point sets in a metric space":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")

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)