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 "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"):
  Abstract: Articulated hand pose and shape estimation is an important problem for vision-based applications such as augmented reality and animation. In contrast to the existing methods which optimize only for joint positions, we propose a fully supervised deep network which learns to jointly estimate a full 3D hand mesh representation and pose from a single depth image. To this end, a CNN architecture is employed to estimate parametric representations i.e. hand pose, bone scales and complex shape parameters. Then, a novel hand pose and shape layer, embedded inside our deep framework, produces 3D joint positions and hand mesh. Lack of sufficient training data with varying hand shapes limits the generalized performance of learning based methods. Also, manually annotating real data is suboptimal. Therefore, we present SynHand5M: a million-scale synthetic dataset with accurate joint annotations, segmentation masks and mesh files of depth maps. Among model based learning (hybrid) methods, we show improved results on our dataset and two of the public benchmarks i.e. NYU and ICVL. Also, by employing a joint training strategy with real and synthetic data, we recover 3D hand mesh and pose from real images in 3.7ms.

Includes members:
  Member "pose guided structured region ensemble network for cascaded hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast and accurate 3d hand pose estimation via recurrent neural network for capturing hand articulations")
  Member "crossing nets combining gans and vaes with a shared latent space for hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast and accurate 3d hand pose estimation via recurrent neural network for capturing hand articulations")
  Member "occlusion aware hand pose estimation using hierarchical mixture density network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding interesting frames in deep video analytics a top k approach")
  Member "learning to estimate 3d hand pose from single rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "using a single rgb frame for real time 3d hand pose estimation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient convolutional neural networks for depth based multi person pose estimation")
  Member "hand3d hand pose estimation using 3d neural network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generalized feedback loop for joint hand object pose estimation")
  Member "simultaneous hand pose and skeleton bone lengths estimation from a single depth image":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "structure from articulated motion accurate and stable monocular 3d reconstruction without training data")

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)