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: "skeleton based action recognition of people handling objects"
  Abstract: In visual surveillance systems, it is necessary to recognize the behavior of people handling objects such as a phone, a cup, or a plastic bag. In this paper, to address this problem, we propose a new framework for recognizing object-related human actions by graph convolutional networks using human and object poses. In this framework, we construct skeletal graphs of reliable human poses by selectively sampling the informative frames in a video, which include human joints with high confidence scores obtained in pose estimation. The skeletal graphs generated from the sampled frames represent human poses related to the object position in both the spatial and temporal domains, and these graphs are used as inputs to the graph convolutional networks. Through experiments over an open benchmark and our own data sets, we verify the validity of our framework in that our method outperforms the state-of-the-art method for skeleton-based action recognition.

Connected through hyperedge HE-1 (induced by source paper "follow the attention combining partial pose and object motion for fine grained action detection"):
  with members:
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "sample efficient deep reinforcement learning for dialogue systems with large action spaces"
    - "simple baselines for human pose estimation and tracking"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "densepose dense human pose estimation in the wild"

Connected through hyperedge HE-2 (induced by source paper "spatio temporal pyramid graph convolutions for human action recognition and postural assessment"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "toward ergonomic risk prediction via segmentation of indoor object manipulation actions using spatiotemporal convolutional networks"
    - "unsupervised discovery of parts structure and dynamics"
    - "open set domain adaptation for image and action recognition"
    - "a comprehensive survey on graph neural networks"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "graph neural networks a review of methods and applications"

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)