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 "on learning 3d face morphable model from in the wild images"):
  Abstract: As a classic statistical model of 3D facial shape and albedo, 3D Morphable Model (3DMM) is widely used in facial analysis, e.g., model fitting, image synthesis. Conventional 3DMM is learned from a set of well-controlled 2D face images with associated 3D face scans, and represented by two sets of PCA basis functions. Due to the type and amount of training data, as well as, the linear bases, the representation power of 3DMM can be limited. To address these problems, this paper proposes an innovative framework to learn a nonlinear 3DMM model from a large set of unconstrained face images, without collecting 3D face scans. Specifically, given a face image as input, a network encoder estimates the projection, illumination, shape and albedo parameters. Two decoders serve as the nonlinear 3DMM to map from the shape and albedo parameters to the 3D shape and albedo, respectively. With the projection parameter, lighting, 3D shape, and albedo, a novel analytically-differentiable rendering layer is designed to reconstruct the original input face. The entire network is end-to-end trainable with only weak supervision. We demonstrate the superior representation power of our nonlinear 3DMM over its linear counterpart, and its contribution to face alignment and 3D reconstruction.

Includes members:
  Member "representation learning by rotating your faces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "face alignment in full pose range a 3d total solution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "disentangling features in 3d face shapes for joint face reconstruction and recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transferable recognition aware image processing")
  Member "unsupervised training for 3d morphable model regression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inverse graphics gan learning to generate 3d shapes from unstructured 2d data")
  Member "joint 3d face reconstruction and dense alignment with position map regression network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "searching central difference convolutional networks for face anti spoofing")
  Member "large pose 3d face reconstruction from a single image via direct volumetric cnn regression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "audio driven talking face video generation with learning based personalized head pose")
  Member "end to end 3d face reconstruction with deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "training efficient network architecture and weights via direct sparsity control")
  Member "learning dense facial correspondences in unconstrained images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dual grid net hand mesh vertex regression from single depth maps")

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)