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 reconstruct people in clothing from a single rgb camera"):
  Abstract: We present a learning-based model to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving, in less than 10 seconds with a reconstruction accuracy of 5mm. Our model learns to predict the parameters of a statistical body model and instance displacements that add clothing and hair to the shape. The model achieves fast and accurate predictions based on two key design choices. First, by predicting shape in a canonical T-pose space, the network learns to encode the images of the person into pose-invariant latent codes, where the information is fused. Second, based on the observation that feed-forward predictions are fast but do not always align with the input images, we predict using both, bottom-up and top-down streams (one per view) allowing information to flow in both directions. Learning relies only on synthetic 3D data. Once learned, the model can take a variable number of frames as input, and is able to reconstruct shapes even from a single image with an accuracy of 6mm. Results on 3 different datasets demonstrate the efficacy and accuracy of our approach.

Includes members:
  Member "generating 3d faces using convolutional mesh autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "learning to estimate 3d human pose and shape from a single color image":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "bodynet volumetric inference of 3d human body shapes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "instance level human parsing via part grouping network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "total capture a 3d deformation model for tracking faces hands and bodies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepcap monocular human performance capture using weak supervision")
  Member "deep autoencoder for combined human pose estimation and body model upscaling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial based neural networks for affect estimations in the wild")
  Member "video based reconstruction of 3d people models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "neural body fitting unifying deep learning and model based human pose and shape estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")

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)