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 "deep single view 3d object reconstruction with visual hull embedding"):
  Abstract: 3D object reconstruction is a fundamental task of many robotics and AI problems. With the aid of deep convolutional neural networks (CNNs), 3D object reconstruction has witnessed a significant progress in recent years. However, possibly due to the prohibitively high dimension of the 3D object space, the results from deep CNNs are often prone to missing some shape details. In this paper, we present an approach which aims to preserve more shape details and improve the reconstruction quality. The key idea of our method is to leverage object mask and pose estimation from CNNs to assist the 3D shape learning by constructing a probabilistic single-view visual hull inside of the network. Our method works by first predicting a coarse shape as well as the object pose and silhouette using CNNs, followed by a novel 3D refinement CNN which refines the coarse shapes using the constructed probabilistic visual hulls. Experiment on both synthetic data and real images show that embedding a single-view visual hull for shape refinement can significantly improve the reconstruction quality by recovering more shapes details and improving shape consistency with the input image.

Includes members:
  Member "3d prnn generating shape primitives with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "learning a multi view stereo machine":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "octree generating networks efficient convolutional architectures for high resolution 3d outputs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "hierarchical surface prediction for 3d object reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "surfacenet an end to end 3d neural network for multiview stereopsis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "rethinking reprojection closing the loop for pose aware shapereconstruction from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "conditional single view shape generation for multi view stereo reconstruction")
  Member "weakly supervised 3d reconstruction with adversarial constraint":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")

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)