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: "step spatio temporal progressive learning for video action detection"
  Abstract: In this paper, we propose Spatio-TEmporal Progressive (STEP) action detector---a progressive learning framework for spatio-temporal action detection in videos. Starting from a handful of coarse-scale proposal cuboids, our approach progressively refines the proposals towards actions over a few steps. In this way, high-quality proposals (i.e., adhere to action movements) can be gradually obtained at later steps by leveraging the regression outputs from previous steps. At each step, we adaptively extend the proposals in time to incorporate more related temporal context. Compared to the prior work that performs action detection in one run, our progressive learning framework is able to naturally handle the spatial displacement within action tubes and therefore provides a more effective way for spatio-temporal modeling. We extensively evaluate our approach on UCF101 and AVA, and demonstrate superior detection results. Remarkably, we achieve mAP of 75.0% and 18.6% on the two datasets with 3 progressive steps and using respectively only 11 and 34 initial proposals.

Connected through hyperedge HE-1 (induced by source paper "actions as moving points"):
  with members:
    - "tacnet transition aware context network for spatio temporal action detection"
    - "centernet keypoint triplets for object detection"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "simple baselines for human pose estimation and tracking"
    - "actor centric relation network"

Connected through hyperedge HE-2 (induced by source paper "stage spatio temporal attention on graph entities for video action detection"):
  with members:
    - "collaborative spatio temporal feature learning for video action recognition"
    - "actor conditioned attention maps for video action detection"
    - "long term feature banks for detailed video understanding"
    - "a better baseline for ava"
    - "actor centric relation network"
    - "video action transformer network"
    - "timeception for complex action recognition"
    - "a structured model for action detection"

Connected through hyperedge HE-3 (induced by source paper "you only watch once a unified cnn architecture for real time spatiotemporal action localization"):
  with members:
    - "resource efficient 3d convolutional neural networks"
    - "eco efficient convolutional network for online video understanding"
    - "videocapsulenet a simplified network for action detection"
    - "predicting action tubes"
    - "cbam convolutional block attention module"
    - "dual attention network for scene segmentation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"

Connected through hyperedge HE-4 (induced by source paper "a delay metric for video object detection what average precision fails to tell"):
  with members:
    - "minimum delay object detection from video"
    - "catdet cascaded tracked detector for efficient object detection from video"
    - "object detection in video with spatiotemporal sampling networks"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "optimizing video object detection via a scale time lattice"
    - "detect or track towards cost effective video object detection tracking"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

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)