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: "two stream collaborative learning with spatial temporal attention for video classification"
  Abstract: Video classification is highly important with wide applications, such as video search and intelligent surveillance. Video naturally consists of static and motion information, which can be represented by frame and optical flow. Recently, researchers generally adopt the deep networks to capture the static and motion information \textbf{\emph{separately}}, which mainly has two limitations: (1) Ignoring the coexistence relationship between spatial and temporal attention, while they should be jointly modelled as the spatial and temporal evolutions of video, thus discriminative video features can be extracted.(2) Ignoring the strong complementarity between static and motion information coexisted in video, while they should be collaboratively learned to boost each other. For addressing the above two limitations, this paper proposes the approach of two-stream collaborative learning with spatial-temporal attention (TCLSTA), which consists of two models: (1) Spatial-temporal attention model: The spatial-level attention emphasizes the salient regions in frame, and the temporal-level attention exploits the discriminative frames in video. They are jointly learned and mutually boosted to learn the discriminative static and motion features for better classification performance. (2) Static-motion collaborative model: It not only achieves mutual guidance on static and motion information to boost the feature learning, but also adaptively learns the fusion weights of static and motion streams, so as to exploit the strong complementarity between static and motion information to promote video classification. Experiments on 4 widely-used datasets show that our TCLSTA approach achieves the best performance compared with more than 10 state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "spatio temporal ranked attention networks for video captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "less is more picking informative frames for video captioning"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "the sound of pixels"
    - "improved image captioning via policy gradient optimization of spider"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "heuristic black box adversarial attacks on video recognition models"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "real world anomaly detection in surveillance videos"
    - "sparse adversarial perturbations for videos"
    - "black box adversarial attacks with limited queries and information"
    - "query efficient hard label black box attack an optimization based approach"
    - "adversarial perturbations against real time video classification systems"

Connected through hyperedge HE-3 (induced by source paper "video captioning with text based dynamic attention and step by step learning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "memory attended recurrent network for video captioning"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "reconstruction network for video captioning"
    - "end to end video captioning with multitask reinforcement learning"
    - "saliency guided video classification via adaptively weighted learning"
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"

Connected through hyperedge HE-4 (induced by source paper "self paced video data augmentation with dynamic images generated by generative adversarial networks"):
  with members:
    - "few shot video classification via temporal alignment"
    - "tganv2 efficient training of large models for video generation with multiple subsampling layers"
    - "recycle gan unsupervised video retargeting"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "spectral normalization for generative adversarial networks"
    - "towards high resolution video generation with progressive growing of sliced wasserstein gans"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "hidden two stream convolutional networks for action recognition"

Connected through hyperedge HE-5 (induced by source paper "deep dual relation modeling for egocentric interaction recognition"):
  with members:
    - "a correlation based feature representation for first person activity recognition"
    - "unsupervised learning of depth and ego motion from video"
    - "learning robot activities from first person human videos using convolutional future regression"
    - "convolutional long short term memory networks for recognizing first person interactions"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "going deeper into first person activity recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "spatial transformer 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)