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 "object aware guidance for autonomous scene reconstruction"):
  Abstract: Object-aware guidance enables automatic exploration, reconstruction, and understanding of unknown scenes in one navigation pass for mobile robots.

Includes members:
  Member "scannet richly annotated 3d reconstructions of indoor scenes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a review on deep learning techniques for 3d sensed data classification")
  Member "pointnet deep hierarchical feature learning on point sets in a metric space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "joint 2d 3d semantic data for indoor scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "semantic scene completion from a single depth image":
    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 "motion planning strategies for autonomously mapping 3d structures":
    also connected through other hyperedges:
      (none)
  Member "3dmatch learning local geometric descriptors from rgb d reconstructions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scan2mesh from unstructured range scans to 3d meshes")
  Member "3dmatch learning the matching of local 3d geometry in range scans":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "teaser fast and certifiable point cloud registration")
  Member "semanticpaint interactive 3d labeling and learning at your fingertips":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation")

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.GR(Graphics)