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: "learning spatio temporal representation with local and global diffusion"
  Abstract: Convolutional Neural Networks (CNN) have been regarded as a powerful class of models for visual recognition problems. Nevertheless, the convolutional filters in these networks are local operations while ignoring the large-range dependency. Such drawback becomes even worse particularly for video recognition, since video is an information-intensive media with complex temporal variations. In this paper, we present a novel framework to boost the spatio-temporal representation learning by Local and Global Diffusion (LGD). Specifically, we construct a novel neural network architecture that learns the local and global representations in parallel. The architecture is composed of LGD blocks, where each block updates local and global features by modeling the diffusions between these two representations. Diffusions effectively interact two aspects of information, i.e., localized and holistic, for more powerful way of representation learning. Furthermore, a kernelized classifier is introduced to combine the representations from two aspects for video recognition. Our LGD networks achieve clear improvements on the large-scale Kinetics-400 and Kinetics-600 video classification datasets against the best competitors by 3.5% and 0.7%. We further examine the generalization of both the global and local representations produced by our pre-trained LGD networks on four different benchmarks for video action recognition and spatio-temporal action detection tasks. Superior performances over several state-of-the-art techniques on these benchmarks are reported. Code is available at: this https URL.

Connected through hyperedge HE-1 (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"
    - "eco efficient convolutional network for online video understanding"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "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-2 (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"
    - "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"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "a short note about kinetics 600"
    - "tsm temporal shift module for efficient video understanding"

Connected through hyperedge HE-3 (induced by source paper "class feature pyramids for video explanation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "video classification with channel separated convolutional networks"
    - "saliency tubes visual explanations for spatio temporal convolutions"
    - "large scale weakly supervised pre training for video action recognition"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "multigrain a unified image embedding for classes and instances"
    - "grounding visual explanations"
    - "multi fiber networks for video recognition"

Connected through hyperedge HE-4 (induced by source paper "decoupling localization and classification in single shot temporal action detection"):
  with members:
    - "gaussian temporal awareness networks for action localization"
    - "jointly localizing and describing events for dense video captioning"
    - "ctap complementary temporal action proposal generation"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-5 (induced by source paper "teinet towards an efficient architecture for video recognition"):
  with members:
    - "spatial group wise enhance improving semantic feature learning in convolutional networks"
    - "eco efficient convolutional network for online video understanding"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "videos as space time region graphs"
    - "slowfast networks for video recognition"
    - "tsm temporal shift module for efficient video understanding"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-6 (induced by source paper "trimmed action recognition dense captioning events in videos and spatio temporal action localization with focus on activitynet challenge 2019"):
  with members:
    - "temporal deformable convolutional encoder decoder networks for video captioning"
    - "pointing novel objects in image captioning"
    - "to create what you tell generating videos from captions"
    - "jointly localizing and describing events for dense video captioning"
    - "exploring visual relationship for image captioning"
    - "attention is all you need"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "deformable convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "vireojd mm at activity detection in extended videos"):
  with members:
    - "exploring object relation in mean teacher for cross domain detection"
    - "trimmed action recognition dense captioning events in videos and spatio temporal action localization with focus on activitynet challenge 2019"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "soft nms improving object detection with one line of code"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "temporal convolution based action proposal submission to activitynet 2017"
    - "deep quantization encoding convolutional activations with deep generative model"
    - "temporal action localization in untrimmed videos via multi stage cnns"

Connected through hyperedge HE-8 (induced by source paper "higher order network for action recognition"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "eco efficient convolutional network for online video understanding"
    - "video action transformer network"
    - "on the effectiveness of task granularity for transfer learning"
    - "videos as space time region graphs"
    - "slowfast networks for video recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"

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)