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 "bi stream pose guided region ensemble network for fingertip localization from stereo images"):
  Abstract: In human-computer interaction, it is important to accurately estimate the hand pose especially fingertips. However, traditional approaches for fingertip localization mainly rely on depth images and thus suffer considerably from the noise and missing values. Instead of depth images, stereo images can also provide 3D information of hands and promote 3D hand pose estimation. There are nevertheless limitations on the dataset size, global viewpoints, hand articulations and hand shapes in the publicly available stereo-based hand pose datasets. To mitigate these limitations and promote further research on hand pose estimation from stereo images, we propose a new large-scale binocular hand pose dataset called THU-Bi-Hand, offering a new perspective for fingertip localization. In the THU-Bi-Hand dataset, there are 447k pairs of stereo images of different hand shapes from 10 subjects with accurate 3D location annotations of the wrist and five fingertips. Captured with minimal restriction on the range of hand motion, the dataset covers large global viewpoint space and hand articulation space. To better present the performance of fingertip localization on THU-Bi-Hand, we propose a novel scheme termed Bi-stream Pose Guided Region Ensemble Network (Bi-Pose-REN). It extracts more representative feature regions around joint points in the feature maps under the guidance of the previously estimated pose. The feature regions are integrated hierarchically according to the topology of hand joints to regress the refined hand pose. Bi-Pose-REN and several existing methods are evaluated on THU-Bi-Hand so that benchmarks are provided for further research. Experimental results show that our new method has achieved the best performance on THU-Bi-Hand.

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 "interactive hand pose estimation boosting accuracy in localizing extended finger joints":
    also connected through other hyperedges:
      (none)
  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 "intel realsense stereoscopic depth cameras":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "infrared and 3d skeleton feature fusion for rgb d action recognition")
  Member "back to rgb 3d tracking of hands and hand object interactions based on short baseline stereo":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "region ensemble network improving convolutional network for hand pose estimation":
    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 "bighand2 2m benchmark hand pose dataset and state of the art analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "handaugment a simple data augmentation method for depth based 3d hand pose estimation")
  Member "depth based 3d hand pose estimation from current achievements to future goals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rotation invariant mixed graphical model network for 2d 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)