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: "a general framework for edited video and raw video summarization"
  Abstract: In this paper, we build a general summarization framework for both of edited video and raw video summarization. Overall, our work can be divided into three folds: 1) Four models are designed to capture the properties of video summaries, i.e., containing important people and objects (importance), representative to the video content (representativeness), no similar key-shots (diversity) and smoothness of the storyline (storyness). Specifically, these models are applicable to both edited videos and raw videos. 2) A comprehensive score function is built with the weighted combination of the aforementioned four models. Note that the weights of the four models in the score function, denoted as property-weight, are learned in a supervised manner. Besides, the property-weights are learned for edited videos and raw videos, respectively. 3) The training set is constructed with both edited videos and raw videos in order to make up the lack of training data. Particularly, each training video is equipped with a pair of mixing-coefficients which can reduce the structure mess in the training set caused by the rough mixture. We test our framework on three datasets, including edited videos, short raw videos and long raw videos. Experimental results have verified the effectiveness of the proposed framework.

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"
    - "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 "video skimming taxonomy and comprehensive survey"):
  with members:
    - "hierarchical recurrent neural network for 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-3 (induced by source paper "video summarization using fully convolutional sequence networks"):
  with members:
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "convolutional sequence to sequence learning"
    - "collaborative summarization of topic related videos"
    - "temporal convolutional networks for action segmentation and detection"
    - "energy based generative adversarial network"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "summary transfer exemplar based subset selection for video summarization"

Connected through hyperedge HE-4 (induced by source paper "a joint convolutional neural networks and context transfer for street scenes labeling"):
  with members:
    - "anomaly detection in traffic scenes via spatial aware motion reconstruction"
    - "automatic subspace learning via principal coefficients embedding"
    - "deep image scaling up image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "simultaneous detection and segmentation"
    - "some improvements on deep convolutional neural network based image classification"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "summarizing first person videos from third persons points of views"):
  with members:
    - "video summarization with attention based encoder decoder networks"
    - "toward storytelling from visual lifelogging an overview"
    - "time contrastive networks self supervised learning from video"
    - "video summarization with long short term memory"
    - "youtube 8m a large scale video classification benchmark"
    - "video2gif automatic generation of animated gifs from video"
    - "domain separation networks"
    - "discovering picturesque highlights from egocentric vacation videos"

Connected through hyperedge HE-6 (induced by source paper "for your eyes only learning to summarize first person videos"):
  with members:
    - "video summarization with attention based encoder decoder networks"
    - "video summarization with long short term memory"
    - "video2gif automatic generation of animated gifs from video"
    - "predicting important objects for egocentric video summarization"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "deep metric learning using triplet network"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-7 (induced by source paper "video summarization with attention based encoder decoder networks"):
  with members:
    - "discovery of shared semantic spaces for multiscene video query and summarization"
    - "learning visual storylines with skipping recurrent neural networks"
    - "summary transfer exemplar based subset selection for video summarization"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"

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)