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 "3d instance segmentation via multi task metric learning"):
  Abstract: We propose a novel method for instance label segmentation of dense 3D voxel grids. We target volumetric scene representations, which have been acquired with depth sensors or multi-view stereo methods and which have been processed with semantic 3D reconstruction or scene completion methods. The main task is to learn shape information about individual object instances in order to accurately separate them, including connected and incompletely scanned objects. We solve the 3D instance-labeling problem with a multi-task learning strategy. The first goal is to learn an abstract feature embedding, which groups voxels with the same instance label close to each other while separating clusters with different instance labels from each other. The second goal is to learn instance information by densely estimating directional information of the instance's center of mass for each voxel. This is particularly useful to find instance boundaries in the clustering post-processing step, as well as, for scoring the segmentation quality for the first goal. Both synthetic and real-world experiments demonstrate the viability and merits of our approach. In fact, it achieves state-of-the-art performance on the ScanNet 3D instance segmentation benchmark.

Includes members:
  Member "learning object bounding boxes for 3d instance segmentation on point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review deep learning on 3d point clouds")
  Member "pixor real time 3d object detection from point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "review deep learning on 3d point clouds")
  Member "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "occuseg occupancy aware 3d instance segmentation")
  Member "masc multi scale affinity with sparse convolution for 3d instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "occuseg occupancy aware 3d instance segmentation")
  Member "panopticfusion online volumetric semantic mapping at the level of stuff and things":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "3d sis 3d semantic instance segmentation of rgb d scans":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep hough voting for 3d object detection in point clouds")
  Member "3dmv joint 3d multi view prediction for 3d semantic scene segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust 6d object pose estimation by learning rgb d features")
  Member "gspn generative shape proposal network for 3d instance segmentation in point cloud":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "review deep learning on 3d point clouds")

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)