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 "what does 2d geometric information really tell us about 3d face shape"):
  Abstract: A face image contains geometric cues in the form of configurational information and contours that can be used to estimate 3D face shape. While it is clear that 3D reconstruction from 2D points is highly ambiguous if no further constraints are enforced, one might expect that the face-space constraint solves this problem. We show that this is not the case and that geometric information is an ambiguous cue. There are two sources for this ambiguity. The first is that, within the space of 3D face shapes, there are flexibility modes that remain when some parts of the face are fixed. The second occurs only under perspective projection and is a result of perspective transformation as camera distance varies. Two different faces, when viewed at different distances, can give rise to the same 2D geometry. To demonstrate these ambiguities, we develop new algorithms for fitting a 3D morphable model to 2D landmarks or contours under either orthographic or perspective projection and show how to compute flexibility modes for both cases. We show that both fitting problems can be posed as a separable nonlinear least squares problem and solved efficiently. We provide quantitative and qualitative evidence that the ambiguity exists in synthetic data and real images.

Includes members:
  Member "learning to regress 3d face shape and expression from an image without 3d supervision":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards high fidelity 3d face reconstruction from in the wild images using graph convolutional networks")
  Member "densereg fully convolutional dense shape regression in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning nonparametric human mesh reconstruction from a single image without ground truth meshes")
  Member "unsupervised training for 3d morphable model regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "inverse graphics gan learning to generate 3d shapes from unstructured 2d data")
  Member "convolutional point set representation a convolutional bridge between a densely annotated image and 3d face alignment":
    also connected through other hyperedges:
      (none)
  Member "evaluation of dense 3d reconstruction from 2d face images in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to regress 3d face shape and expression from an image without 3d supervision")
  Member "nonlinear 3d face morphable model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "audio driven talking face video generation with learning based personalized head pose")
  Member "learning dense facial correspondences in unconstrained images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dual grid net hand mesh vertex regression from single depth maps")
  Member "unrestricted facial geometry reconstruction using image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the whole is greater than the sum of its nonrigid parts")

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)