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: "human pose estimation in space and time using 3d cnn"
  Abstract: This paper explores the capabilities of convolutional neural networks to deal with a task that is easily manageable for humans: perceiving 3D pose of a human body from varying angles. However, in our approach, we are restricted to using a monocular vision system. For this purpose, we apply a convolutional neural network approach on RGB videos and extend it to three dimensional convolutions. This is done via encoding the time dimension in videos as the 3\(^\mathrm{rd}\) dimension in convolutional space, and directly regressing to human body joint positions in 3D coordinate space. This research shows the ability of such a network to achieve state-of-the-art performance on the selected Human3.6M dataset, thus demonstrating the possibility of successfully representing temporal data with an additional dimension in the convolutional operation.

Connected through hyperedge HE-1 (induced by source paper "dynamic kernel distillation for efficient pose estimation in videos"):
  with members:
    - "simple baselines for human pose estimation and tracking"
    - "began boundary equilibrium generative adversarial networks"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "end to end representation learning for correlation filter based tracking"
    - "lstm pose machines"
    - "self adversarial training for human pose estimation"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "detect and track efficient pose estimation in videos"

Connected through hyperedge HE-2 (induced by source paper "monocap monocular human motion capture using a cnn coupled with a geometric prior"):
  with members:
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "stacked hourglass networks for human pose estimation"
    - "mocap guided data augmentation for 3d pose estimation in the wild"
    - "3d human pose estimation using convolutional neural networks with 2d pose information"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"
    - "synthesizing training images for boosting human 3d pose estimation"
    - "deep kinematic pose regression"
    - "sparseness meets deepness 3d human pose estimation from monocular video"

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)