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: "action recognition for depth video using multi view dynamic images"
  Abstract: Dynamic imaging is a recently proposed action description paradigm for simultaneously capturing motion and temporal evolution information, particularly in the context of deep convolutional neural networks (CNNs). Compared with optical flow for motion characterization, dynamic imaging exhibits superior efficiency and compactness. Inspired by the success of dynamic imaging in RGB video, this study extends it to the depth domain. To better exploit three-dimensional (3D) characteristics, multi-view dynamic images are proposed. In particular, the raw depth video is densely projected with respect to different virtual imaging viewpoints by rotating the virtual camera within the 3D space. Subsequently, dynamic images are extracted from the obtained multi-view depth videos and multi-view dynamic images are thus constructed from these images. Accordingly, more view-tolerant visual cues can be involved. A novel CNN model is then proposed to perform feature learning on multi-view dynamic images. Particularly, the dynamic images from different views share the same convolutional layers but correspond to different fully connected layers. This is aimed at enhancing the tuning effectiveness on shallow convolutional layers by alleviating the gradient vanishing problem. Moreover, as the spatial occurrence variation of the actions may impair the CNN, an action proposal approach is also put forth. In experiments, the proposed approach can achieve state-of-the-art performance on three challenging datasets.

Connected through hyperedge HE-1 (induced by source paper "view invariant deep architecture for human action recognition using two stream motion and shape temporal dynamics"):
  with members:
    - "compressed video action recognition"
    - "temporal 3d convnets new architecture and transfer learning for video classification"
    - "learning human pose models from synthesized data for robust rgb d action recognition"
    - "skepxels spatio temporal image representation of human skeleton joints for action recognition"
    - "action recognition with coarse to fine deep feature integration and asynchronous fusion"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "learning a deep model for human action recognition from novel viewpoints"
    - "spatio temporal lstm with trust gates for 3d human action recognition"

Connected through hyperedge HE-2 (induced by source paper "dmcl distillation multiple choice learning for multimodal action recognition"):
  with members:
    - "unifying heterogeneous classifiers with distillation"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "learning with privileged information via adversarial discriminative modality distillation"
    - "human action recognition and prediction a survey"
    - "snapshot distillation teacher student optimization in one generation"
    - "motion feature network fixed motion filter for action recognition"
    - "modality distillation with multiple stream networks for action recognition"
    - "representation flow for action recognition"

Connected through hyperedge HE-3 (induced by source paper "multi view region adaptive multi temporal dmm and rgb action recognition"):
  with members:
    - "two stream 3d convolutional neural network for skeleton based action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "towards good practices for very deep two stream convnets"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "a discussion on the validation tests employed to compare human action recognition methods using the msr action3d dataset"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "cross view action modeling learning and recognition"

Connected through hyperedge HE-4 (induced by source paper "rgb d based action recognition with light weight 3d convolutional networks"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "a fine to coarse convolutional neural network for 3d human action recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "modality distillation with multiple stream networks for action recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quo vadis action recognition a new model and the kinetics dataset"

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)