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 "context aware deep spatio temporal network for hand pose estimation from depth images"):
  Abstract: As a fundamental and challenging problem in computer vision, hand pose estimation aims to estimate the hand joint locations from depth images. Typically, the problem is modeled as learning a mapping function from images to hand joint coordinates in a data-driven manner. In this paper, we propose Context-Aware Deep Spatio-Temporal Network (CADSTN), a novel method to jointly model the spatio-temporal properties for hand pose estimation. Our proposed network is able to learn the representations of the spatial information and the temporal structure from the image sequences. Moreover, by adopting adaptive fusion method, the model is capable of dynamically weighting different predictions to lay emphasis on sufficient context. Our method is examined on two common benchmarks, the experimental results demonstrate that our proposed approach achieves the best or the second-best performance with state-of-the-art methods and runs in 60fps.

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 "thin slicing network a deep structured model for pose estimation in videos":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unipose unified human pose estimation in single images and videos")
  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 "back to rgb 3d tracking of hands and hand object interactions based on short baseline stereo":
    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 "deepprior improving fast and accurate 3d hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "3d hand pose estimation in the wild via graph refinement under adversarial learning")

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)