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 "inferring point clouds from single monocular images by depth intermediation"):
  Abstract: In this paper, we propose a framework for generating 3D point cloud of an object from a single-view RGB image. Most previous work predict the 3D point coordinates from single RGB images directly. We decompose this problem into depth estimation from single images and point completion from partial point clouds. #R##N#Our method sequentially predicts the depth maps and then infers the complete 3D object point clouds based on the predicted partial point clouds. We explicitly impose the camera model geometrical constraint in our pipeline and enforce the alignment of the generated point clouds and estimated depth maps. #R##N#Experimental results for the single image 3D object reconstruction task show that the proposed method outperforms state-of-the-art methods. Both the qualitative and quantitative results demonstrate the generality and suitability of our method.

Includes members:
  Member "multi view consistency as supervisory signal for learning shape and pose prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "seqxy2seqz structure learning for 3d shapes by sequentially predicting 1d occupancy segments from 2d coordinates")
  Member "pcn point completion network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sequential forecasting of 100 000 points")
  Member "3d lmnet latent embedding matching for accurate and diverse 3d point cloud reconstruction from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep sketch based modeling of man made shapes")
  Member "3dcontextnet k d tree guided hierarchical learning of point clouds using local and global contextual cues":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "indoor scene understanding in 2 5 3d for autonomous agents a survey")
  Member "mining point cloud local structures by kernel correlation and graph pooling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "3d object reconstruction from a single depth view with adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pf net point fractal network for 3d point cloud completion")
  Member "foldingnet point cloud auto encoder via deep grid deformation":
    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 "weakly supervised 3d reconstruction with adversarial constraint":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "structurenet hierarchical graph networks for 3d shape generation")

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)