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: "linear time online action detection from 3d skeletal data using bags of gesturelets"
  Abstract: Sliding window is one direct way to extend a successful recognition system to handle the more challenging detection problem. While action recognition decides only whether or not an action is present in a pre-segmented video sequence, action detection identifies the time interval where the action occurred in an unsegmented video stream. Sliding window approaches for action detection can however be slow as they maximize a classifier score over all possible sub-intervals. Even though new schemes utilize dynamic programming to speed up the search for the optimal sub-interval, they require offline processing on the whole video sequence. In this paper, we propose a novel approach for online action detection based on 3D skeleton sequences extracted from depth data. It identifies the sub-interval with the maximum classifier score in linear time. Furthermore, it is invariant to temporal scale variations and is suitable for real-time applications with low latency.

Connected through hyperedge HE-1 (induced by source paper "space time representation of people based on 3d skeletal data a review"):
  with members:
    - "simultaneous feature and body part learning for real time robot awareness of human behaviors"
    - "kernelized covariance for action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "combining local appearance and holistic view dual source deep neural networks for human pose estimation"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "a discussion on the validation tests employed to compare human action recognition methods using the msr action3d dataset"
    - "cross view action modeling learning and recognition"
    - "hopc histogram of oriented principal components of 3d pointclouds for action recognition"

Connected through hyperedge HE-2 (induced by source paper "real time online action detection forests using spatio temporal contexts"):
  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"
    - "convolutional two stream network fusion for video action recognition"
    - "end to end learning of action detection from frame glimpses in videos"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "return of the devil in the details delving deep into convolutional nets"
    - "hopc histogram of oriented principal components of 3d pointclouds for action recognition"

Connected through hyperedge HE-3 (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"
    - "real time online action detection forests using spatio temporal contexts"
    - "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)