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 "deepprior improving fast and accurate 3d hand pose estimation"):
  Abstract: DeepPrior is a simple approach based on Deep Learning that predicts the joint 3D locations of a hand given a depth map. Since its publication early 2015, it has been outperformed by several impressive works. Here we show that with simple improvements: adding ResNet layers, data augmentation, and better initial hand localization, we achieve better or similar performance than more sophisticated recent methods on the three main benchmarks (NYU, ICVL, MSRA) while keeping the simplicity of the original method. Our new implementation is available at this https URL .

Includes members:
  Member "learning to estimate 3d hand pose from single rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "hand3d hand pose estimation using 3d neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generalized feedback loop for joint hand object pose estimation")
  Member "region ensemble network improving convolutional network for hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast and accurate 3d hand pose estimation via recurrent neural network for capturing hand articulations")
  Member "end to end global to local cnn learning for hand pose recovery in depth data":
    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 "dynamics based 3d skeletal hand tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast and accurate 3d hand pose estimation via recurrent neural network for capturing hand articulations")
  Member "training a feedback loop for hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "structured prediction of 3d human pose with deep neural networks":
    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)