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 "synthesizing coupled 3d face modalities by trunk branch generative adversarial networks"):
  Abstract: Generating realistic 3D faces is of high importance for computer graphics and computer vision applications. Generally, research on 3D face generation revolves around linear statistical models of the facial surface. Nevertheless, these models cannot represent faithfully either the facial texture or the normals of the face, which are very crucial for photo-realistic face synthesis. Recently, it was demonstrated that Generative Adversarial Networks (GANs) can be used for generating high-quality textures of faces. Nevertheless, the generation process either omits the geometry and normals, or independent processes are used to produce 3D shape information. In this paper, we present the first methodology that generates high-quality texture, shape, and normals jointly, which can be used for photo-realistic synthesis. To do so, we propose a novel GAN that can generate data from different modalities while exploiting their correlations. Furthermore, we demonstrate how we can condition the generation on the expression and create faces with various facial expressions. The qualitative results shown in this pre-print is compressed due to size limitations, full resolution results and the accompanying video can be found at the project page: this https URL.

Includes members:
  Member "meshgan non linear 3d morphable models of faces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dressing 3d humans using a conditional mesh vae gan")
  Member "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "audio driven talking face video generation with learning based personalized head pose")
  Member "representation learning by rotating your faces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "3dfacegan adversarial nets for 3d face representation generation and translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural 3d morphable models spiral convolutional networks for 3d shape representation learning and generation")
  Member "combining 3d morphable models a large scale face and head model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural 3d morphable models spiral convolutional networks for 3d shape representation learning and generation")
  Member "generating 3d faces using convolutional mesh autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inducing optimal attribute representations for conditional gans")
  Member "arcface additive angular margin loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)