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: "temporal 3d convnets new architecture and transfer learning for video classification"
  Abstract: The work in this paper is driven by the question how to exploit the temporal cues available in videos for their accurate classification, and for human action recognition in particular? Thus far, the vision community has focused on spatio-temporal approaches with fixed temporal convolution kernel depths. We introduce a new temporal layer that models variable temporal convolution kernel depths. We embed this new temporal layer in our proposed 3D CNN. We extend the DenseNet architecture - which normally is 2D - with 3D filters and pooling kernels. We name our proposed video convolutional network `Temporal 3D ConvNet'~(T3D) and its new temporal layer `Temporal Transition Layer'~(TTL). Our experiments show that T3D outperforms the current state-of-the-art methods on the HMDB51, UCF101 and Kinetics datasets. #R##N#The other issue in training 3D ConvNets is about training them from scratch with a huge labeled dataset to get a reasonable performance. So the knowledge learned in 2D ConvNets is completely ignored. Another contribution in this work is a simple and effective technique to transfer knowledge from a pre-trained 2D CNN to a randomly initialized 3D CNN for a stable weight initialization. This allows us to significantly reduce the number of training samples for 3D CNNs. Thus, by finetuning this network, we beat the performance of generic and recent methods in 3D CNNs, which were trained on large video datasets, e.g. Sports-1M, and finetuned on the target datasets, e.g. HMDB51/UCF101. The T3D codes will be released

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:
    - "action recognition for depth video using multi view dynamic images"
    - "compressed video action recognition"
    - "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 "risky action recognition in lane change video clips using deep spatiotemporal networks with segmentation mask transfer"):
  with members:
    - "yolov3 an incremental improvement"
    - "camera based vehicle velocity estimation from monocular video"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "universal language model fine tuning for text classification"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "recognizing american sign language manual signs from rgb d videos"):
  with members:
    - "video based sign language recognition without temporal segmentation"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "the kinetics human action video dataset"
    - "rank pooling for action recognition"
    - "evaluation of deep learning based pose estimation for sign language recognition"
    - "beyond temporal pooling recurrence and temporal convolutions for gesture recognition in video"

Connected through hyperedge HE-4 (induced by source paper "looking ahead anticipating pedestrians crossing with future frames prediction"):
  with members:
    - "self driving cars a survey"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "stochastic video generation with a learned prior"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "mocogan decomposing motion and content for video generation"
    - "an overview of multi task learning in deep neural networks"
    - "unsupervised learning for physical interaction through video prediction"

Connected through hyperedge HE-5 (induced by source paper "literature review of action recognition in the wild"):
  with members:
    - "human action recognition and prediction a survey"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "optimized skeleton based action recognition via sparsified graph regression"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"

Connected through hyperedge HE-6 (induced by source paper "self supervised spatiotemporal feature learning via video rotation prediction"):
  with members:
    - "self supervised visual feature learning with deep neural networks a survey"
    - "real world anomaly detection in surveillance videos"
    - "moments in time dataset one million videos for event understanding"
    - "videocapsulenet a simplified network for action detection"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "cross and learn cross modal self supervision"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-7 (induced by source paper "stacked spatio temporal graph convolutional networks for action segmentation"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "end to end fine grained action segmentation and recognition using conditional random field models and discriminative sparse coding"
    - "graph edge convolutional neural networks for skeleton based action recognition"
    - "videos as space time region graphs"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "the description length of deep learning models"
    - "rgcnn regularized graph cnn for point cloud segmentation"

Connected through hyperedge HE-8 (induced by source paper "eco efficient convolutional network for online video understanding"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "temporal segment networks for action recognition in videos"
    - "the kinetics human action video dataset"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "end to end video level representation learning for action recognition"
    - "convnet architecture search for spatiotemporal feature learning"

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)