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: "learning to search on manifolds for 3d pose estimation of articulated objects"
  Abstract: This paper focuses on the challenging problem of 3D pose estimation of a diverse spectrum of articulated objects from single depth images. A novel structured prediction approach is considered, where 3D poses are represented as skeletal models that naturally operate on manifolds. Given an input depth image, the problem of predicting the most proper articulation of underlying skeletal model is thus formulated as sequentially searching for the optimal skeletal configuration. This is subsequently addressed by convolutional neural nets trained end-to-end to render sequential prediction of the joint locations as regressing a set of tangent vectors of the underlying manifolds. Our approach is examined on various articulated objects including human hand, mouse, and fish benchmark datasets. Empirically it is shown to deliver highly competitive performance with respect to the state-of-the-arts, while operating in real-time (over 30 FPS).

Connected through hyperedge HE-1 (induced by source paper "pose guided structured region ensemble network for cascaded hand pose estimation"):
  with members:
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "intel realsense stereoscopic depth cameras"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "end to end global to local cnn learning for hand pose recovery in depth data"
    - "dynamics based 3d skeletal hand tracking"
    - "motion feature augmented recurrent neural network for skeleton based dynamic hand gesture recognition"
    - "mask r cnn"
    - "training a feedback loop for hand pose estimation"

Connected through hyperedge HE-2 (induced by source paper "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "learning to estimate 3d hand pose from single rgb images"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"
    - "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map"
    - "hand3d hand pose estimation using 3d neural network"
    - "simultaneous hand pose and skeleton bone lengths estimation from a single depth image"

Connected through hyperedge HE-3 (induced by source paper "context aware deep spatio temporal network for hand pose estimation from depth images"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "learning to estimate 3d hand pose from single rgb images"
    - "back to rgb 3d tracking of hands and hand object interactions based on short baseline stereo"
    - "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map"
    - "hand3d hand pose estimation using 3d neural network"
    - "deepprior improving fast and accurate 3d hand pose estimation"

Connected through hyperedge HE-4 (induced by source paper "simultaneous hand pose and skeleton bone lengths estimation from a single depth image"):
  with members:
    - "learning to estimate 3d hand pose from single rgb images"
    - "hand3d hand pose estimation using 3d neural network"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "end to end global to local cnn learning for hand pose recovery in depth data"
    - "training a feedback loop for hand pose estimation"
    - "two stream convolutional neural network for accurate rgb d fingertip detection using depth and edge information"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"
    - "spatial attention deep net with partial pso for hierarchical hybrid hand pose estimation"

Connected through hyperedge HE-5 (induced by source paper "region ensemble network improving convolutional network for hand pose estimation"):
  with members:
    - "training a feedback loop for hand pose estimation"
    - "two stream convolutional neural network for accurate rgb d fingertip detection using depth and edge information"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"
    - "network of experts for large scale image categorization"
    - "human pose estimation via convolutional part heatmap regression"
    - "model based deep hand pose estimation"
    - "deeptrack learning discriminative feature representations online for robust visual tracking"
    - "deep residual learning for image recognition"

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)