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 carigan an end to end solution to 3d caricature generation from face photos"):
  Abstract: Caricature is a kind of artistic style of human faces that attracts considerable research in computer vision. So far all existing 3D caricature generation methods require some information related to caricature as input, e.g., a caricature sketch or 2D caricature. However, this kind of input is difficult to provide by non-professional users. In this paper, we propose an end-to-end deep neural network model to generate high-quality 3D caricature with a simple face photo as input. The most challenging issue in our system is that the source domain of face photos (characterized by 2D normal faces) is significantly different from the target domain of 3D caricatures (characterized by 3D exaggerated face shapes and texture). To address this challenge, we (1) build a large dataset of 6,100 3D caricature meshes and use it to establish a PCA model in the 3D caricature shape space and (2) detect landmarks in the input face photo and use them to set up correspondence between 2D caricature and 3D caricature shape. Our system can automatically generate high-quality 3D caricatures. In many situations, users want to control the output by a simple and intuitive way, so we further introduce a simple-to-use interactive control with three horizontal and one vertical lines. Experiments and user studies show that our system is easy to use and can generate high-quality 3D caricatures.

Includes members:
  Member "carigans unpaired photo to caricature translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ganhopper multi hop gan for unsupervised image to image translation")
  Member "maskgan towards diverse and interactive facial image manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "carigan caricature generation through weakly paired adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fine grained image to image transformation towards visual recognition")
  Member "generating 3d faces using convolutional mesh autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "warpgan automatic caricature generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mw gan multi warping gan for caricature generation with multi style geometric exaggeration")
  Member "alive caricature from 2d to 3d":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "3d magic mirror automatic video to 3d caricature translation")
  Member "3d face reconstruction with geometry details from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "audio driven talking face video generation with learning based personalized head pose")
  Member "large pose 3d face reconstruction from a single image via direct volumetric cnn regression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "audio driven talking face video generation with learning based personalized head pose")

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)