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: "stnet local and global spatial temporal modeling for action recognition"
  Abstract: Despite the success of deep learning for static image understanding, it remains unclear what are the most effective network architectures for the spatial-temporal modeling in videos. In this paper, in contrast to the existing CNN+RNN or pure 3D convolution based approaches, we explore a novel spatial temporal network (StNet) architecture for both local and global spatial-temporal modeling in videos. Particularly, StNet stacks N successive video frames into a \emph{super-image} which has 3N channels and applies 2D convolution on super-images to capture local spatial-temporal relationship. To model global spatial-temporal relationship, we apply temporal convolution on the local spatial-temporal feature maps. Specifically, a novel temporal Xception block is proposed in StNet. It employs a separate channel-wise and temporal-wise convolution over the feature sequence of video. Extensive experiments on the Kinetics dataset demonstrate that our framework outperforms several state-of-the-art approaches in action recognition and can strike a satisfying trade-off between recognition accuracy and model complexity. We further demonstrate the generalization performance of the leaned video representations on the UCF101 dataset.

Connected through hyperedge HE-1 (induced by source paper "dynamic inference a new approach toward efficient video action recognition"):
  with members:
    - "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition"
    - "eco efficient convolutional network for online video understanding"
    - "multi fiber networks for video recognition"
    - "motion feature network fixed motion filter for action recognition"
    - "tsm temporal shift module for efficient video understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"

Connected through hyperedge HE-2 (induced by source paper "sth spatio temporal hybrid convolution for efficient action recognition"):
  with members:
    - "stm spatiotemporal and motion encoding for action recognition"
    - "dynamonet dynamic action and motion network"
    - "learning spatio temporal representation with local and global diffusion"
    - "eco efficient convolutional network for online video understanding"
    - "motion feature network fixed motion filter for action recognition"
    - "videos as space time region graphs"
    - "spatio temporal channel correlation networks for action classification"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-3 (induced by source paper "knowledge integration networks for action recognition"):
  with members:
    - "geometric pose affordance 3d human pose with scene constraints"
    - "eco efficient convolutional network for online video understanding"
    - "temporal bilinear networks for video action recognition"
    - "actor action semantic segmentation with region masks"
    - "videos as space time region graphs"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"

Connected through hyperedge HE-4 (induced by source paper "ctm collaborative temporal modeling for action recognition"):
  with members:
    - "stm spatiotemporal and motion encoding for action recognition"
    - "video classification with channel separated convolutional networks"
    - "learning spatio temporal representation with local and global diffusion"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "eco efficient convolutional network for online video understanding"
    - "multi fiber networks for video recognition"
    - "a short note about kinetics 600"
    - "tsm temporal shift module for efficient video understanding"

Connected through hyperedge HE-5 (induced by source paper "iqiyi submission to activitynet challenge 2019 kinetics 700 challenge hierarchical group wise attention"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multi fiber networks for video recognition"
    - "slowfast networks for video recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-6 (induced by source paper "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition"):
  with members:
    - "read watch and move reinforcement learning for temporally grounding natural language descriptions in videos"
    - "eco efficient convolutional network for online video understanding"
    - "multi fiber networks for video recognition"
    - "fine grained video categorization with redundancy reduction attention"
    - "motion feature network fixed motion filter for action recognition"
    - "compact generalized non local network"
    - "adaframe adaptive frame selection for fast video recognition"
    - "videos as space time region graphs"

Connected through hyperedge HE-7 (induced by source paper "stm spatiotemporal and motion encoding for action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "end to end learning of motion representation for video understanding"
    - "motion feature network fixed motion filter for action recognition"
    - "videos as space time region graphs"
    - "spatio temporal channel correlation networks for action classification"
    - "slowfast networks for video recognition"
    - "tsm temporal shift module for efficient video understanding"

Connected through hyperedge HE-8 (induced by source paper "towards real time action recognition on mobile devices using deep models"):
  with members:
    - "rethinking imagenet pre training"
    - "eco efficient convolutional network for online video understanding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multi fiber networks for video recognition"
    - "spatio temporal channel correlation networks for action classification"
    - "slowfast networks for video recognition"
    - "mnasnet platform aware neural architecture search for mobile"
    - "tsm temporal shift module for efficient video understanding"

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)