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 structure for end to end inverse rendering"):
  Abstract: Inverse rendering in a 3D format denoted to recovering the 3D properties of a scene given 2D input image(s) and is typically done using 3D Morphable Model (3DMM) based methods from single view images. These models formulate each face as a weighted combination of some basis vectors extracted from the training data. In this paper a deep framework is proposed in which the coefficients and basis vectors are computed by training an autoencoder network and a Convolutional Neural Network (CNN) simultaneously. The idea is to find a common cause which can be mapped to both the 3D structure and corresponding 2D image using deep networks. The empirical results verify the power of deep framework in finding accurate 3D shapes of human faces from their corresponding 2D images on synthetic datasets of human faces.

Includes members:
  Member "large pose 3d face reconstruction from a single image via direct volumetric cnn regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "audio driven talking face video generation with learning based personalized head pose")
  Member "sparse representation for 3d shape estimation a convex relaxation approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ego pose estimation and forecasting as real time pd control")
  Member "lifting from the deep convolutional 3d pose estimation from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild")
  Member "3d reconstruction of simple objects from a single view silhouette image":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "apollocar3d a large 3d car instance understanding benchmark for autonomous driving")
  Member "perspective transformer nets learning single view 3d object reconstruction without 3d supervision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "weakly supervised disentangling with recurrent transformations for 3d view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised disentanglement without compromises")
  Member "a simple fast and highly accurate algorithm to recover 3d shape from 2d landmarks on a single image":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sim2real transfer learning for 3d pose estimation motion to the rescue")
  Member "regressing robust and discriminative 3d morphable models with a very deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "face hallucination with finishing touches")

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)