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 object bounding boxes for 3d instance segmentation on point clouds"):
  Abstract: We propose a novel, conceptually simple and general framework for instance segmentation on 3D point clouds. Our method, called 3D-BoNet, follows the simple design philosophy of per-point multilayer perceptrons (MLPs). The framework directly regresses 3D bounding boxes for all instances in a point cloud, while simultaneously predicting a point-level mask for each instance. It consists of a backbone network followed by two parallel network branches for 1) bounding box regression and 2) point mask prediction. 3D-BoNet is single-stage, anchor-free and end-to-end trainable. Moreover, it is remarkably computationally efficient as, unlike existing approaches, it does not require any post-processing steps such as non-maximum suppression, feature sampling, clustering or voting. Extensive experiments show that our approach surpasses existing work on both ScanNet and S3DIS datasets while being approximately 10x more computationally efficient. Comprehensive ablation studies demonstrate the effectiveness of our design.

Includes members:
  Member "3d bird s eye view instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bi directional attention for joint instance and semantic segmentation in point clouds")
  Member "4d spatio temporal convnets minkowski convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  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 "kpconv flexible and deformable convolution for point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "associatively segmenting instances and semantics in point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "review deep learning on 3d point clouds")
  Member "masc multi scale affinity with sparse convolution for 3d instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "occuseg occupancy aware 3d instance segmentation")
  Member "monte carlo convolution for learning on non uniformly sampled point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pointasnl robust point clouds processing using nonlocal neural networks with adaptive sampling")
  Member "deep hough voting for 3d object detection in point clouds":
    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)