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 "silhouette net 3d hand pose estimation from silhouettes"):
  Abstract: 3D hand pose estimation has received a lot of attention for its wide range of applications and has made great progress owing to the development of deep learning. Existing approaches mainly consider different input modalities and settings, such as monocular RGB, multi-view RGB, depth, or point cloud, to provide sufficient cues for resolving variations caused by self occlusion and viewpoint change. In contrast, this work aims to address the less-explored idea of using minimal information to estimate 3D hand poses. We present a new architecture that automatically learns a guidance from implicit depth perception and solves the ambiguity of hand pose through end-to-end training. The experimental results show that 3D hand poses can be accurately estimated from solely {\em hand silhouettes} without using depth maps. Extensive evaluations on the {\em 2017 Hands In the Million Challenge} (HIM2017) benchmark dataset further demonstrate that our method achieves comparable or even better performance than recent depth-based approaches and serves as the state-of-the-art of its own kind on estimating 3D hand poses from silhouettes.

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 "augmented skeleton space transfer for depth based hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rotation invariant mixed graphical model network for 2d hand 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 "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 "lifting from the deep convolutional 3d pose estimation from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild")
  Member "hand3d hand pose estimation using 3d neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generalized feedback loop for joint hand object pose estimation")
  Member "deepprior improving fast and accurate 3d hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "3d hand pose estimation in the wild via graph refinement under adversarial learning")
  Member "towards good practices for deep 3d hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "handaugment a simple data augmentation method for depth based 3d hand pose estimation")

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)