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: "video summarization with attention based encoder decoder networks"
  Abstract: This paper addresses the problem of supervised video summarization by formulating it as a sequence-to-sequence learning problem, where the input is a sequence of original video frames, the output is a keyshot sequence. Our key idea is to learn a deep summarization network with attention mechanism to mimic the way of selecting the keyshots of human. To this end, we propose a novel video summarization framework named Attentive encoder-decoder networks for Video Summarization (AVS), in which the encoder uses a Bidirectional Long Short-Term Memory (BiLSTM) to encode the contextual information among the input video frames. As for the decoder, two attention-based LSTM networks are explored by using additive and multiplicative objective functions, respectively. Extensive experiments are conducted on three video summarization benchmark datasets, i.e., SumMe, and TVSum. The results demonstrate the superiority of the proposed AVS-based approaches against the state-of-the-art approaches,with remarkable improvements from 0.8% to 3% on two datasets,respectively..

Connected through hyperedge HE-1 (induced by source paper "meta learning for task driven video summarization"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "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"
    - "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 "video skimming taxonomy and comprehensive survey"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "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"
    - "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-3 (induced by source paper "video summarization via actionness ranking"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "temporal action detection with structured segment 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-4 (induced by source paper "transparency by design closing the gap between performance and interpretability in visual reasoning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning to count objects in natural images for visual question answering"
    - "compositional attention networks for machine reasoning"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"
    - "sensor transformation attention networks"
    - "segmentation aware convolutional networks using local attention masks"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-5 (induced by source paper "salient object detection in video using deep non local neural networks"):
  with members:
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "deep visual attention prediction"
    - "video salient object detection using spatiotemporal deep features"
    - "review of visual saliency detection with comprehensive information"
    - "deepvs a deep learning based video saliency prediction approach"
    - "difnet semantic segmentation by diffusion networks"
    - "non local recurrent network for image restoration"

Connected through hyperedge HE-6 (induced by source paper "dilated temporal relational adversarial network for generic video summarization"):
  with members:
    - "viewpoint aware video summarization"
    - "how local is the local diversity reinforcing sequential determinantal point processes with dynamic ground sets for supervised video summarization"
    - "dual motion gan for future flow embedded video prediction"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "collaborative summarization of topic related videos"
    - "rethinking atrous convolution for semantic image segmentation"
    - "disentangling factors of variation in deep representations using adversarial training"

Connected through hyperedge HE-7 (induced by source paper "multi stream dynamic video summarization"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "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"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "multi view surveillance video summarization via joint embedding and sparse optimization"

Connected through hyperedge HE-8 (induced by source paper "summarizing videos with attention"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "amnet memorability estimation with attention"
    - "learning video summarization using unpaired data"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "a structured self attentive sentence embedding"
    - "reflections on shannon information in search of a natural information entropy for images"

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)