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: "adaptive graphical model network for 2d handpose estimation"
  Abstract: In this paper, we propose a new architecture called Adaptive Graphical Model Network (AGMN) to tackle the task of 2D hand pose estimation from a monocular RGB image. The AGMN consists of two branches of deep convolutional neural networks for calculating unary and pairwise potential functions, followed by a graphical model inference module for integrating unary and pairwise potentials. Unlike existing architectures proposed to combine DCNNs with graphical models, our AGMN is novel in that the parameters of its graphical model are conditioned on and fully adaptive to individual input images. Experiments show that our approach outperforms the state-of-the-art method used in 2D hand keypoints estimation by a notable margin on two public datasets.

Connected through hyperedge HE-1 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "geometric pose affordance 3d human pose with scene constraints"
    - "3d hand shape and pose from images in the wild"
    - "deep high resolution representation learning for human pose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "multi scale structure aware network for human pose estimation"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "hand pose estimation via latent 2 5d heatmap regression"

Connected through hyperedge HE-2 (induced by source paper "nonparametric structure regularization machine for 2d hand pose estimation"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "multi scale structure aware network for human pose estimation"
    - "exploiting temporal information for 3d human pose estimation"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "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"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

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)