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: "summary transfer exemplar based subset selection for video summarization"
  Abstract: Video summarization has unprecedented importance to help us digest, browse, and search today's ever-growing video collections. We propose a novel subset selection technique that leverages supervision in the form of human-created summaries to perform automatic keyframe-based video summarization. The main idea is to nonparametrically transfer summary structures from annotated videos to unseen test videos. We show how to extend our method to exploit semantic side information about the video's category/genre to guide the transfer process by those training videos semantically consistent with the test input. We also show how to generalize our method to subshot-based summarization, which not only reduces computational costs but also provides more flexible ways of defining visual similarity across subshots spanning several frames. We conduct extensive evaluation on several benchmarks and demonstrate promising results, outperforming existing methods in several settings.

Connected through hyperedge HE-1 (induced by source paper "video storytelling textual summaries for events"):
  with members:
    - "ffnet video fast forwarding via reinforcement learning"
    - "predicting visual features from text for image and video caption retrieval"
    - "unsupervised learning of view invariant action representations"
    - "query adaptive video summarization via quality aware relevance estimation"
    - "improved image captioning via policy gradient optimization of spider"
    - "dual glance model for deciphering social relationships"
    - "summarization of user generated sports video by using deep action recognition features"
    - "attention transfer from web images for video recognition"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "scsampler sampling salient clips from video for efficient action recognition"):
  with members:
    - "video classification with channel separated convolutional networks"
    - "long term feature banks for detailed video understanding"
    - "learning discriminative video representations using adversarial perturbations"
    - "learning to separate object sounds by watching unlabeled video"
    - "adaframe adaptive frame selection for fast video recognition"
    - "slowfast networks for video recognition"
    - "the sound of pixels"
    - "cooperative learning of audio and video models from self supervised synchronization"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (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"
    - "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"
    - "video summarization with long short term memory"
    - "actionness estimation using hybrid fully convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "hierarchical recurrent neural network for video summarization"):
  with members:
    - "video summarization with long short term memory"
    - "title generation for user generated videos"
    - "beyond short snippets deep networks for video classification"
    - "hierarchical recurrent neural encoder for video representation with application to captioning"
    - "sequence to sequence video to text"
    - "unsupervised extraction of video highlights via robust recurrent auto encoders"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "rethinking the evaluation of video summaries"):
  with members:
    - "query adaptive video summarization via quality aware relevance estimation"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "real time video highlights for yahoo esports"
    - "video summarization with long short term memory"
    - "query focused extractive video summarization"
    - "video summarization using deep semantic features"
    - "videoset video summary evaluation through text"

Connected through hyperedge HE-8 (induced by source paper "demystifying multi faceted video summarization tradeoff between diversity representation coverage and importance"):
  with members:
    - "video2gif automatic generation of animated gifs from video"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "learning mixtures of submodular shells with application to document summarization"

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)