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 "region ensemble network improving convolutional network for hand pose estimation"):
  Abstract: Hand pose estimation from monocular depth images is an important and challenging problem for human-computer interaction. Recently deep convolutional networks (ConvNet) with sophisticated design have been employed to address it, but the improvement over traditional methods is not so apparent. To promote the performance of directly 3D coordinate regression, we propose a tree-structured Region Ensemble Network (REN), which partitions the convolution outputs into regions and integrates the results from multiple regressors on each regions. Compared with multi-model ensemble, our model is completely end-to-end training. The experimental results demonstrate that our approach achieves the best performance among state-of-the-arts on two public datasets.

Includes members:
  Member "training a feedback loop for hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "two stream convolutional neural network for accurate rgb d fingertip detection using depth and edge information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficiently utilizing complex valued polsar image data via a multi task deep learning framework")
  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-3 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "learning to search on manifolds for 3d pose estimation of articulated objects":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pose guided structured region ensemble network for cascaded hand pose estimation")
  Member "network of experts for large scale image categorization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "continual learning in neural networks")
  Member "human pose estimation via convolutional part heatmap regression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unipose unified human pose estimation in single images and videos")
  Member "model based deep hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast and accurate 3d hand pose estimation via recurrent neural network for capturing hand articulations")
  Member "deeptrack learning discriminative feature representations online for robust visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "high performance visual object tracking with unified convolutional networks")

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)