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 "single view object shape reconstruction using deep shape prior and silhouette"):
  Abstract: 3D shape reconstruction from a single image is a highly ill-posed problem. Modern deep learning based systems try to solve this problem by learning an end-to-end mapping from image to shape via a deep network. In this paper, we aim to solve this problem via an online optimization framework inspired by traditional methods. Our framework employs a deep autoencoder to learn a set of latent codes of 3D object shapes, which are fitted by a probabilistic shape prior using Gaussian Mixture Model (GMM). At inference, the shape and pose are jointly optimized guided by both image cues and deep shape prior without relying on an initialization from any trained deep nets. Surprisingly, our method achieves comparable performance to state-of-the-art methods even without training an end-to-end network, which shows a promising step in this direction.

Includes members:
  Member "codeslam learning a compact optimisable representation for dense visual slam":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "3d interpreter networks for viewer centered wireframe modeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semi parametric object synthesis")
  Member "3d pose estimation and 3d model retrieval for objects in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised deep metric learning via auxiliary rotation loss")
  Member "atlasnet a papier m ach e approach to learning 3d surface generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "learning to generate and reconstruct 3d meshes with only 2d supervision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency")
  Member "unsupervised learning of shape and pose with differentiable point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "multi view consistency as supervisory signal for learning shape and pose prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "seqxy2seqz structure learning for 3d shapes by sequentially predicting 1d occupancy segments from 2d coordinates")
  Member "pix3d dataset and methods for single image 3d shape modeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "total3dunderstanding joint layout object pose and mesh reconstruction for indoor scenes from a single image")

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)