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: "hierarchical recurrent neural network for video summarization"
  Abstract: Exploiting the temporal dependency among video frames or subshots is very important for the task of video summarization. Practically, RNN is good at temporal dependency modeling, and has achieved overwhelming performance in many video-based tasks, such as video captioning and classification. However, RNN is not capable enough to handle the video summarization task, since traditional RNNs, including LSTM, can only deal with short videos, while the videos in the summarization task are usually in longer duration. To address this problem, we propose a hierarchical recurrent neural network for video summarization, called H-RNN in this paper. Specifically, it has two layers, where the first layer is utilized to encode short video subshots cut from the original video, and the final hidden state of each subshot is input to the second layer for calculating its confidence to be a key subshot. Compared to traditional RNNs, H-RNN is more suitable to video summarization, since it can exploit long temporal dependency among frames, meanwhile, the computation operations are significantly lessened. The results on two popular datasets, including the Combined dataset and VTW dataset, have demonstrated that the proposed H-RNN outperforms the state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "meta learning for task driven video summarization"):
  with members:
    - "a general framework for edited video and raw video summarization"
    - "patch alignment manifold matting"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "video summarization with attention based encoder decoder networks"
    - "meta sgd learning to learn quickly for few shot learning"
    - "collaborative summarization of topic related videos"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-2 (induced by source paper "what i see is what you see joint attention learning for first and third person video co analysis"):
  with members:
    - "boosted attention leveraging human attention for image captioning"
    - "object part attention model for fine grained image classification"
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "semantic aware attention based deep object co segmentation"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "cross view image synthesis using conditional gans"
    - "harmonious attention network for person re identification"
    - "global pose estimation with an attention based recurrent network"

Connected through hyperedge HE-3 (induced by source paper "vision to language tasks based on attributes and attention mechanism"):
  with members:
    - "show ask attend and answer a strong baseline for visual question answering"
    - "attend to you personalized image captioning with context sequence memory networks"
    - "tgif qa toward spatio temporal reasoning in visual question answering"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "dual attention networks for multimodal reasoning and matching"
    - "image captioning with semantic attention"
    - "hierarchical question image co attention for visual question answering"
    - "dynamic memory networks for visual and textual question answering"

Connected through hyperedge HE-4 (induced by source paper "video skimming taxonomy and comprehensive survey"):
  with members:
    - "a general framework for edited video and raw video summarization"
    - "phd gifs personalized highlight detection for automatic gif creation"
    - "discovery of shared semantic spaces for multiscene video query and summarization"
    - "summarizing first person videos from third persons points of views"
    - "video summarization with attention based encoder decoder networks"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "collaborative summarization of topic related videos"
    - "summary transfer exemplar based subset selection for video summarization"

Connected through hyperedge HE-5 (induced by source paper "scar spatial channel wise attention regression networks for crowd counting"):
  with members:
    - "weakly supervised adversarial domain adaptation for semantic segmentation in urban scenes"
    - "c 3 framework an open source pytorch code for crowd counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "crowd counting using deep recurrent spatial aware network"
    - "exploring models and data for remote sensing image caption generation"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"

Connected through hyperedge HE-6 (induced by source paper "video summarization via actionness ranking"):
  with members:
    - "temporal action detection with structured segment networks"
    - "video summarization with attention based encoder decoder networks"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "searching action proposals via spatial actionness estimation and temporal path inference and tracking"
    - "temporal activity detection in untrimmed videos with recurrent neural networks"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "actionness estimation using hybrid fully convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "few shot adaptation for multimedia semantic indexing"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "the kinetics human action video dataset"
    - "zero shot learning the good the bad and the ugly"
    - "learning a deep embedding model for zero shot learning"
    - "places an image database for deep scene understanding"
    - "synthesized classifiers for zero shot learning"
    - "matching networks for one shot learning"

Connected through hyperedge HE-8 (induced by source paper "multi stream dynamic video summarization"):
  with members:
    - "video summarization via actionness ranking"
    - "how local is the local diversity reinforcing sequential determinantal point processes with dynamic ground sets for supervised video summarization"
    - "improving sequential determinantal point processes for supervised video summarization"
    - "diversity aware multi video summarization"
    - "identifying first person camera wearers in third person videos"
    - "video summarization with attention based encoder decoder networks"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "multi view surveillance video summarization via joint embedding and sparse optimization"

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)