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 "pix3d dataset and methods for single image 3d shape modeling"):
  Abstract: We study 3D shape modeling from a single image and make contributions to it in three aspects. First, we present Pix3D, a large-scale benchmark of diverse image-shape pairs with pixel-level 2D-3D alignment. Pix3D has wide applications in shape-related tasks including reconstruction, retrieval, viewpoint estimation, etc. Building such a large-scale dataset, however, is highly challenging; existing datasets either contain only synthetic data, or lack precise alignment between 2D images and 3D shapes, or only have a small number of images. Second, we calibrate the evaluation criteria for 3D shape reconstruction through behavioral studies, and use them to objectively and systematically benchmark cutting-edge reconstruction algorithms on Pix3D. Third, we design a novel model that simultaneously performs 3D reconstruction and pose estimation; our multi-task learning approach achieves state-of-the-art performance on both tasks.

Includes members:
  Member "octree generating networks efficient convolutional architectures for high resolution 3d outputs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "t less an rgb d dataset for 6d pose estimation of texture less objects":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "hierarchical surface prediction for 3d object reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "weakly supervised 3d reconstruction with adversarial constraint":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "structurenet hierarchical graph networks for 3d shape generation")
  Member "multi view supervision for single view reconstruction via differentiable ray consistency":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "learning 3d object categories by looking around them":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "indoor scene understanding in 2 5 3d for autonomous agents a survey")
  Member "large scale 3d shape reconstruction and segmentation from shapenet core55":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "instant recovery of shape from spectrum via latent space connections")
  Member "semantic scene completion from a single depth image":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")

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)