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 "fastv2c handnet fast voxel to coordinate hand pose estimation with 3d convolutional neural networks"):
  Abstract: Hand pose estimation from monocular depth images has been an important and challenging problem in the Computer Vision community. In this paper, we present a novel approach to estimate 3D hand joint locations from 2D depth images. Unlike most of the previous methods, our model captures the 3D spatial information from a depth image thereby giving it a greater understanding of the input. We voxelize the input depth map to capture the 3D features of the input and perform 3D data augmentations to make our network robust to real-world images. Our network is trained in an end-to-end manner which reduces time and space complexity significantly when compared to other methods. Through extensive experiments, we show that our model outperforms state-of-the-art methods with respect to the time it takes to train and predict 3D hand joint locations. This makes our method more suitable for real-world hand pose estimation scenarios.

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 "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d hand pose estimation in the wild via graph refinement under adversarial learning")
  Member "single image 3d hand reconstruction with mesh convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3d hand pose estimation in the wild via graph refinement under adversarial learning")
  Member "augmented skeleton space transfer for depth based hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "hand pose estimation via latent 2 5d heatmap regression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "metric scale truncation robust heatmaps for 3d human 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-6 (induced by source paper "fast and accurate 3d hand pose estimation via recurrent neural network for capturing hand articulations")
  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 "region ensemble network improving convolutional network for hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fast and accurate 3d hand pose estimation via recurrent neural network for capturing hand articulations")

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)