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: "real time online action detection forests using spatio temporal contexts"
  Abstract: Online action detection (OAD) is challenging since 1) robust yet computationally expensive features cannot be straightforwardly used due to the real-time processing requirements and 2) the localization and classification of actions have to be performed even before they are fully observed. We propose a new random forest (RF)-based online action detection framework that addresses these challenges. Our algorithm uses computationally efficient skeletal joint features. High accuracy is achieved by using robust convolutional neural network (CNN)-based features which are extracted from the raw RGBD images, plus the temporal relationships between the current frame of interest, and the past and future frames. While these high-quality features are not available in real-time testing scenario, we demonstrate that they can be effectively exploited in training RF classifiers: We use these spatio-temporal contexts to craft RF's new split functions improving RFs' leaf node statistics. Experiments with challenging MSRAction3D, G3D, and OAD datasets demonstrate that our algorithm significantly improves the accuracy over the state-of-the-art online action detection algorithms while achieving the real-time efficiency of existing skeleton-based RF classifiers.

Connected through hyperedge HE-1 (induced by source paper "skeleton based online action prediction using scale selection network"):
  with members:
    - "leveraging structural context models and ranking score fusion for human interaction prediction"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "skeleton boxes solving skeleton based action detection with a single deep convolutional neural network"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cascaded boundary regression for temporal action detection"
    - "temporal context network for activity localization in videos"

Connected through hyperedge HE-2 (induced by source paper "a proposed artificial intelligence model for real time human action localization and tracking"):
  with members:
    - "two stream lstm a deep fusion framework for human action recognition"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "online human action detection using joint classification regression recurrent neural networks"
    - "deep learning for detecting multiple space time action tubes in videos"
    - "online real time multiple spatiotemporal action localisation and prediction"
    - "finding action tubes"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "augmented skeleton space transfer for depth based hand pose estimation"):
  with members:
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "surfnet generating 3d shape surfaces using deep residual networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dynamics based 3d skeletal hand tracking"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "first person hand action benchmark with rgb d videos and 3d hand pose annotations"

Connected through hyperedge HE-4 (induced by source paper "learning and refining of privileged information based rnns for action recognition from depth sequences"):
  with members:
    - "bayesian joint modelling for object localisation in weakly labelled images"
    - "disturblabel regularizing cnn on the loss layer"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "attend infer repeat fast scene understanding with generative models"
    - "bayesian dark knowledge"
    - "a recurrent latent variable model for sequential data"

Connected through hyperedge HE-5 (induced by source paper "transition forests learning discriminative temporal transitions for action recognition and detection"):
  with members:
    - "online action detection"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "online human action detection using joint classification regression recurrent neural networks"
    - "linear time online action detection from 3d skeletal data using bags of gesturelets"
    - "differential recurrent neural networks for action recognition"

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)