human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "depth based hand pose estimation methods data and challenges"
  Abstract: Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame. To do so, we have implemented a considerable number of systems, and will release all software and evaluation code. We summarize important conclusions here: (1) Pose estimation appears roughly solved for scenes with isolated hands. However, methods still struggle to analyze cluttered scenes where hands may be interacting with nearby objects and surfaces. To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes. (2) Many methods evaluate themselves with disparate criteria, making comparisons difficult. We define a consistent evaluation criteria, rigorously motivated by human experiments. (3) We introduce a simple nearest-neighbor baseline that outperforms most existing systems. This implies that most systems do not generalize beyond their training sets. This also reinforces the under-appreciated point that training data is as important as the model itself. We conclude with directions for future progress.

Connected through hyperedge HE-1 (induced by source paper "end to end global to local cnn learning for hand pose recovery in depth data"):
  with members:
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "convolutional pose machines"
    - "training a feedback loop for hand pose estimation"
    - "maximum margin structured learning with deep networks for 3d human pose estimation"
    - "combining local appearance and holistic view dual source deep neural networks for human pose estimation"
    - "robust optimization for deep regression"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-2 (induced by source paper "deep kinematic pose regression"):
  with members:
    - "convolutional pose machines"
    - "training a feedback loop for hand pose estimation"
    - "stacked hourglass networks for human pose estimation"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"
    - "structured prediction of 3d human pose with deep neural networks"
    - "deep deformation network for object landmark localization"
    - "model based deep hand pose estimation"
    - "sparseness meets deepness 3d human pose estimation from monocular video"

Connected through hyperedge HE-3 (induced by source paper "spatial attention deep net with partial pso for hierarchical hybrid hand pose estimation"):
  with members:
    - "training a feedback loop for hand pose estimation"
    - "theano a python framework for fast computation of mathematical expressions"
    - "spatial transformer networks"
    - "fast and robust hand tracking using detection guided optimization"
    - "draw a recurrent neural network for image generation"
    - "hands deep in deep learning for hand pose estimation"
    - "attention for fine grained categorization"
    - "deeppose human pose estimation via deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "model based deep hand pose estimation"):
  with members:
    - "training a feedback loop for hand pose estimation"
    - "see the difference direct pre image reconstruction and pose estimation by differentiating hog"
    - "fast and robust hand tracking using detection guided optimization"
    - "hybrid one shot 3d hand pose estimation by exploiting uncertainties"
    - "hands deep in deep learning for hand pose estimation"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "hand pose estimation through semi supervised and weakly supervised learning"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "hand3d hand pose estimation using 3d neural network"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "end to end global to local cnn learning for hand pose recovery in depth data"
    - "learning from synthetic humans"
    - "training a feedback loop for hand pose estimation"
    - "stacked hourglass networks for human pose estimation"

Question: Please tell me which class the center node 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)