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: "efficient two stream motion and appearance 3d cnns for video classification"
  Abstract: The video and action classification have extremely evolved by deep neural networks specially with two stream CNN using RGB and optical flow as inputs and they present outstanding performance in terms of video analysis. One of the shortcoming of these methods is handling motion information extraction which is done out side of the CNNs and relatively time consuming also on GPUs. So proposing end-to-end methods which are exploring to learn motion representation, like 3D-CNN can achieve faster and accurate performance. We present some novel deep CNNs using 3D architecture to model actions and motion representation in an efficient way to be accurate and also as fast as real-time. Our new networks learn distinctive models to combine deep motion features into appearance model via learning optical flow features inside the network.

Connected through hyperedge HE-1 (induced by source paper "dynamonet dynamic action and motion network"):
  with members:
    - "visual dynamics stochastic future generation via layered cross convolutional networks"
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "self supervised learning of face representations for video face clustering"
    - "learning human optical flow"
    - "spatio temporal channel correlation networks for action classification"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "d3d distilled 3d networks for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "two stream region convolutional 3d network for temporal activity detection"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "focal loss for dense object detection"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "temporal context network for activity localization in videos"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "tube convolutional neural network t cnn for action detection in videos"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal channel correlation networks for action classification"):
  with members:
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "look listen and learn"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "squeeze and excitation networks"
    - "convnet architecture search for spatiotemporal feature learning"

Connected through hyperedge HE-4 (induced by source paper "coupled recurrent network crn"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "temporal segment networks for action recognition in videos"
    - "integrating both visual and audio cues for enhanced video caption"
    - "lattice long short term memory for human action recognition"
    - "convolutional pose machines"
    - "real time action recognition with enhanced motion vector cnns"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-5 (induced by source paper "optical flow guided feature a fast and robust motion representation for video action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning feature pyramids for human pose estimation"
    - "lattice long short term memory for human action recognition"
    - "rank pooling for action recognition"
    - "multi context attention for human pose estimation"
    - "convnet architecture search for spatiotemporal feature learning"
    - "convolutional pose machines"

Connected through hyperedge HE-6 (induced by source paper "temporal segment networks for action recognition in videos"):
  with members:
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
    - "the kinetics human action video dataset"
    - "the thumos challenge on action recognition for videos in the wild"
    - "real time action recognition with enhanced motion vector cnns"
    - "depth2action exploring embedded depth for large scale action recognition"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-7 (induced by source paper "hidden two stream convolutional networks for action recognition"):
  with members:
    - "video enhancement with task oriented flow"
    - "deep texture manifold for ground terrain recognition"
    - "learning optical flow via dilated networks and occlusion reasoning"
    - "towards universal representation for unseen action recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "deep local video feature for action recognition"

Connected through hyperedge HE-8 (induced by source paper "activity recognition based on a magnitude orientation stream network"):
  with members:
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "unsupervised learning of video representations using lstms"
    - "human action recognition using factorized spatio temporal convolutional networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "towards good practices for very deep two stream convnets"
    - "going deeper with convolutions"

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)