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: "simple yet efficient real time pose based action recognition"
  Abstract: Recognizing human actions is a core challenge for autonomous systems as they directly share the same space with humans. Systems must be able to recognize and assess human actions in real-time. In order to train corresponding data-driven algorithms, a significant amount of annotated training data is required. We demonstrated a pipeline to detect humans, estimate their pose, track them over time and recognize their actions in real-time with standard monocular camera sensors. For action recognition, we encode the human pose into a new data format called Encoded Human Pose Image (EHPI) that can then be classified using standard methods from the computer vision community. With this simple procedure we achieve competitive state-of-the-art performance in pose-based action detection and can ensure real-time performance. In addition, we show a use case in the context of autonomous driving to demonstrate how such a system can be trained to recognize human actions using simulation data.

Connected through hyperedge HE-1 (induced by source paper "skeleton based activity recognition by fusing part wise spatio temporal and attention driven residues"):
  with members:
    - "skeletal movement to color map a novel representation for 3d action recognition with inception residual networks"
    - "exploiting deep residual networks for human action recognition from skeletal data"
    - "attention is all you need"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "massive exploration of neural machine translation architectures"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"

Connected through hyperedge HE-2 (induced by source paper "make skeleton based action recognition model smaller faster and better"):
  with members:
    - "can wifi estimate person pose"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "simple baselines for human pose estimation and tracking"
    - "slowfast networks for video recognition"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "skeleton based action recognition using lstm and cnn"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"

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)