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 sic 3d semantic instance completion for rgb d scans"):
  Abstract: This paper focuses on the task of semantic instance completion: from an incomplete, RGB-D scan of a scene, we aim to detect the individual object instances comprising the scene and jointly infer their complete object geometry. This enables a semantically meaningful decomposition of a scanned scene into individual, complete 3D objects. This semantic instance completion of a 3D scene opens up many new possibilities in enabling meaningful interactions with a scene, for instance for virtual or robotic agents. Rather than considering 3D semantic instance segmentation and scan completion separately, we propose 3D-SIC, a new end-to-end 3D convolutional neural network which jointly learns to detect object instances and predict their complete geometry -- achieving significantly better performance than treating these tasks independently. 3D-SIC leverages joint color-geometry feature learning and a fully-convolutional 3D network to effectively infer semantic instance completion for 3D scans at scale. Our method runs at interactive rates, taking several seconds inference time on scenes of $30$m $\times$ $25$m spatial extent. For the task of semantic instance completion, we additionally introduce a new semantic instance completion benchmark on real scan data, where we outperform alternative approaches by over 15 in mAP@0.5.

Includes members:
  Member "3dmv joint 3d multi view prediction for 3d semantic scene segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "review deep learning on 3d point clouds")
  Member "scan2cad learning cad model alignment in rgb d scans":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "imvotenet boosting 3d object detection in point clouds with image votes")
  Member "high resolution shape completion using deep neural networks for global structure and local geometry inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "sgpn similarity group proposal network for 3d point cloud instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "review deep learning on 3d point clouds")
  Member "frustum pointnets for 3d object detection from rgb d data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review deep learning on 3d point clouds")
  Member "pointnet deep hierarchical feature learning on point sets in a metric space":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "hierarchical surface prediction for 3d object reconstruction":
    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)