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: "online human action detection using joint classification regression recurrent neural networks"
  Abstract: Human action recognition from well-segmented 3D skeleton data has been intensively studied and has been attracting an increasing attention. Online action detection goes one step further and is more challenging, which identifies the action type and localizes the action positions on the fly from the untrimmed stream data. In this paper, we study the problem of online action detection from streaming skeleton data. We propose a multi-task end-to-end Joint Classification-Regression Recurrent Neural Network to better explore the action type and temporal localization information. By employing a joint classification and regression optimization objective, this network is capable of automatically localizing the start and end points of actions more accurately. Specifically, by leveraging the merits of the deep Long Short-Term Memory (LSTM) subnetwork, the proposed model automatically captures the complex long-range temporal dynamics, which naturally avoids the typical sliding window design and thus ensures high computational efficiency. Furthermore, the subtask of regression optimization provides the ability to forecast the action prior to its occurrence. To evaluate our proposed model, we build a large streaming video dataset with annotations. Experimental results on our dataset and the public G3D dataset both demonstrate very promising performance of our scheme.

Connected through hyperedge HE-1 (induced by source paper "a novel online action detection framework from untrimmed video streams"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "temporal action detection with structured segment networks"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "unsupervised learning for physical interaction through video prediction"
    - "online action detection"
    - "segmental spatiotemporal cnns for fine grained action segmentation"
    - "generating videos with scene dynamics"

Connected through hyperedge HE-2 (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-3 (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"
    - "skeleton based action recognition of people handling objects"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "delving into 3d action anticipation from streaming videos"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "on the integration of optical flow and action recognition"
    - "temporal action detection with structured segment networks"
    - "encouraging lstms to anticipate actions very early"

Connected through hyperedge HE-5 (induced by source paper "ddlstm dual domain lstm for cross dataset action recognition"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "autodial automatic domain alignment layers"
    - "lstm a search space odyssey"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep hashing network for unsupervised domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "bonn activity maps dataset description"):
  with members:
    - "efficient 3d reconstruction and streaming for group scale multi client live telepresence"
    - "a dataset of multi illumination images in the wild"
    - "the replica dataset a digital replica of indoor spaces"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "latticenet fast point cloud segmentation using permutohedral lattices"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "joint 2d 3d semantic data for indoor scene understanding"

Connected through hyperedge HE-7 (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"
    - "deep learning for detecting multiple space time action tubes in videos"
    - "online real time multiple spatiotemporal action localisation and prediction"
    - "real time online action detection forests using spatio temporal contexts"
    - "finding action tubes"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "online detection of action start in untrimmed streaming videos"):
  with members:
    - "multi fiber networks for video recognition"
    - "ctap complementary temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "good semi supervised learning that requires a bad gan"
    - "single shot temporal action detection"
    - "predictive corrective networks for action detection"
    - "temporal action detection with structured segment networks"
    - "cascaded boundary regression for temporal action 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)