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: "initialization strategies of spatio temporal convolutional neural networks"
  Abstract: We propose a new way of incorporating temporal information present in videos into Spatial Convolutional Neural Networks (ConvNets) trained on images, that avoids training Spatio-Temporal ConvNets from scratch. We describe several initializations of weights in 3D Convolutional Layers of Spatio-Temporal ConvNet using 2D Convolutional Weights learned from ImageNet. We show that it is important to initialize 3D Convolutional Weights judiciously in order to learn temporal representations of videos. We evaluate our methods on the UCF-101 dataset and demonstrate improvement over Spatial ConvNets.

Connected through hyperedge HE-1 (induced by source paper "evolving space time neural architectures for videos"):
  with members:
    - "learning discriminative video representations using adversarial perturbations"
    - "regularized evolution for image classifier architecture search"
    - "temporal gaussian mixture layer for videos"
    - "video representation learning using discriminative pooling"
    - "mnasnet platform aware neural architecture search for mobile"
    - "large scale evolution of image classifiers"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"

Connected through hyperedge HE-2 (induced by source paper "multimodal feature fusion for cnn based gait recognition an empirical comparison"):
  with members:
    - "end to end incremental learning"
    - "deep residual learning for image recognition"
    - "learning human identity from motion patterns"
    - "multimodal deep learning for robust rgb d object recognition"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-3 (induced by source paper "quo vadis action recognition a new model and the kinetics dataset"):
  with members:
    - "the kinetics human action video dataset"
    - "deep learning for detecting multiple space time action tubes in videos"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "spatiotemporal residual networks for video action recognition"
    - "videolstm convolves attends and flows for action recognition"
    - "recurrent batch normalization"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-4 (induced by source paper "order aware convolutional pooling for video based action recognition"):
  with members:
    - "discriminatively learned hierarchical rank pooling networks"
    - "rank pooling for action recognition"
    - "real time action recognition with enhanced motion vector cnns"
    - "temporal segment networks towards good practices for deep action recognition"
    - "long term temporal convolutions for action recognition"
    - "human action recognition using factorized spatio temporal convolutional networks"
    - "beyond short snippets deep networks for video classification"
    - "action recognition with trajectory pooled deep convolutional descriptors"

Connected through hyperedge HE-5 (induced by source paper "every moment counts dense detailed labeling of actions in complex videos"):
  with members:
    - "recognizing fine grained and composite activities using hand centric features and script data"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "unsupervised learning of video representations using lstms"
    - "beyond short snippets deep networks for video classification"
    - "show attend and tell neural image caption generation with visual attention"
    - "temporal pyramid pooling based convolutional neural networks for action recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-6 (induced by source paper "temporal pyramid pooling based convolutional neural networks for action recognition"):
  with members:
    - "return of the devil in the details delving deep into convolutional nets"
    - "bag of visual words and fusion methods for action recognition comprehensive study and good practice"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "two stream convolutional networks for action recognition in videos"
    - "learning latent spatio temporal compositional model for human action recognition"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-7 (induced by source paper "direct prediction of 3d body poses from motion compensated sequences"):
  with members:
    - "flying objects detection from a single moving camera"
    - "robust estimation of 3d human poses from a single image"

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)