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 "learning to regress 3d face shape and expression from an image without 3d supervision"):
  Abstract: The estimation of 3D face shape from a single image must be robust to variations in lighting, head pose, expression, facial hair, makeup, and occlusions. Robustness requires a large training set of in-the-wild images, which by construction, lack ground truth 3D shape. To train a network without any 2D-to-3D supervision, we present RingNet, which learns to compute 3D face shape from a single image. Our key observation is that an individual's face shape is constant across images, regardless of expression, pose, lighting, etc. RingNet leverages multiple images of a person and automatically detected 2D face features. It uses a novel loss that encourages the face shape to be similar when the identity is the same and different for different people. We achieve invariance to expression by representing the face using the FLAME model. Once trained, our method takes a single image and outputs the parameters of FLAME, which can be readily animated. Additionally we create a new database of faces `not quite in-the-wild' (NoW) with 3D head scans and high-resolution images of the subjects in a wide variety of conditions. We evaluate publicly available methods and find that RingNet is more accurate than methods that use 3D supervision. The dataset, model, and results are available for research purposes at this http URL.

Includes members:
  Member "expressive body capture 3d hands face and body from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "movi a large multipurpose motion and video dataset")
  Member "generating 3d faces using convolutional mesh autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  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 "joint 3d face reconstruction and dense alignment with position map regression network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "searching central difference convolutional networks for face anti spoofing")
  Member "evaluation of dense 3d reconstruction from 2d face images in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wing loss for robust facial landmark localisation with convolutional neural networks")
  Member "nonlinear 3d face morphable model":
    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 "vggface2 a dataset for recognising faces across pose and age":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  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)