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 "rgb based 3d hand pose estimation via privileged learning with depth images"):
  Abstract: This paper proposes a method for hand pose estimation from RGB images that uses both external large-scale depth image datasets and paired depth and RGB images as privileged information at training time. We show that providing depth information during training significantly improves performance of pose estimation from RGB images during testing. We explore different ways of using this privileged information: (1) using depth data to initially train a depth-based network, (2) using the features from the depth-based network of the paired depth images to constrain mid-level RGB network weights, and (3) using the foreground mask, obtained from the depth data, to suppress the responses from the background area. By using paired RGB and depth images, we are able to supervise the RGB-based network to learn middle layer features that mimic that of the corresponding depth-based network, which is trained on large-scale, accurately annotated depth data. During testing, when only an RGB image is available, our method produces accurate 3D hand pose predictions. Our method is also tested on 2D hand pose estimation. Experiments on three public datasets show that the method outperforms the state-of-the-art methods for hand pose estimation using RGB image input.

Includes members:
  Member "domain transfer for 3d pose estimation from color images without manual annotations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "honnotate a method for 3d annotation of hand and objects poses")
  Member "hand pose estimation via latent 2 5d heatmap regression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "metric scale truncation robust heatmaps for 3d human pose estimation")
  Member "cross modal deep variational hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "crossing nets combining gans and vaes with a shared latent space for hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast and accurate 3d hand pose estimation via recurrent neural network for capturing hand articulations")
  Member "training group orthogonal neural networks with privileged information":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "orthogonal convolutional neural networks")
  Member "learning to estimate 3d hand pose from single rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "lifting from the deep convolutional 3d pose estimation from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild")

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)