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 "3d morphable models as spatial transformer networks"):
  Abstract: In this paper, we show how a 3D Morphable Model (i.e. a statistical model of the 3D shape of a class of objects such as faces) can be used to spatially transform input data as a module (a 3DMM-STN) within a convolutional neural network. This is an extension of the original spatial transformer network in that we are able to interpret and normalise 3D pose changes and self-occlusions. The trained localisation part of the network is independently useful since it learns to fit a 3D morphable model to a single image. We show that the localiser can be trained using only simple geometric loss functions on a relatively small dataset yet is able to perform robust normalisation on highly uncontrolled images including occlusion, self-occlusion and large pose changes.

Includes members:
  Member "densereg fully convolutional dense shape regression in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning nonparametric human mesh reconstruction from a single image without ground truth meshes")
  Member "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sm nas structural to modular neural architecture search for object detection")
  Member "what does 2d geometric information really tell us about 3d face shape":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning perspective undistortion of portraits")
  Member "large pose 3d face reconstruction from a single image via direct volumetric cnn regression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "audio driven talking face video generation with learning based personalized head pose")
  Member "unrestricted facial geometry reconstruction using image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the whole is greater than the sum of its nonrigid parts")
  Member "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "inversefacenet deep single shot inverse face rendering from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering")
  Member "warped convolutions efficient invariance to spatial transformations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "roto translation equivariant convolutional networks application to histopathology image analysis")

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)