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 "pose guided structured region ensemble network for cascaded hand pose estimation"):
  Abstract: Abstract   Hand pose estimation from single depth images is an essential topic in computer vision and human computer interaction. Despite recent advancements in this area promoted by convolutional neural networks, accurate hand pose estimation is still a challenging problem. In this paper we propose a novel approach named as pose guided structured region ensemble network (Pose-REN) to boost the performance of hand pose estimation. Under the guidance of an initially estimated pose, the proposed method extracts regions from the feature maps of convolutional neural network and generates more optimal and representative features for hand pose estimation. The extracted feature regions are then integrated hierarchically according to the topology of hand joints by tree-structured fully connections to regress the refined hand pose. The final hand pose is obtained by an iterative cascaded method. Comprehensive experiments on public hand pose datasets demonstrate that our proposed method outperforms state-of-the-art algorithms.

Includes members:
  Member "deep alignment network a convolutional neural network for robust face alignment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint face alignment and 3d face reconstruction with application to face recognition")
  Member "intel realsense stereoscopic depth cameras":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "infrared and 3d skeleton feature fusion for rgb d action recognition")
  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 "motion feature augmented recurrent neural network for skeleton based dynamic hand gesture recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vision based body gesture meta features for affective computing")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "training a feedback loop for hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deepim deep iterative matching for 6d 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)