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: "query adaptive video summarization via quality aware relevance estimation"
  Abstract: Although the problem of automatic video summarization has recently received a lot of attention, the problem of creating a video summary that also highlights elements relevant to a search query has been less studied. We address this problem by posing query-relevant summarization as a video frame subset selection problem, which lets us optimise for summaries which are simultaneously diverse, representative of the entire video, and relevant to a text query. We quantify relevance by measuring the distance between frames and queries in a common textual-visual semantic embedding space induced by a neural network. In addition, we extend the model to capture query-independent properties, such as frame quality. We compare our method against previous state of the art on textual-visual embeddings for thumbnail selection and show that our model outperforms them on relevance prediction. Furthermore, we introduce a new dataset, annotated with diversity and query-specific relevance labels. On this dataset, we train and test our complete model for video summarization and show that it outperforms standard baselines such as Maximal Marginal Relevance.

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"
    - "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"
    - "recurrent topic transition gan for visual paragraph generation"

Connected through hyperedge HE-2 (induced by source paper "convolutional hierarchical attention network for query focused video summarization"):
  with members:
    - "viewpoint aware video summarization"
    - "query conditioned three player adversarial network for video summarization"
    - "video summarization using fully convolutional sequence networks"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "query focused extractive video summarization"
    - "video summarization using deep semantic features"

Connected through hyperedge HE-3 (induced by source paper "what i see is what you see joint attention learning for first and third person video co analysis"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "sentence specified dynamic video thumbnail generation"):
  with members:
    - "weakly supervised spatio temporally grounding natural sentence in video"
    - "videos as space time region graphs"
    - "to find where you talk temporal sentence localization in video with attention based location regression"
    - "attention is all you need"
    - "dense captioning events in videos"
    - "tall temporal activity localization via language query"
    - "localizing moments in video with natural language"
    - "to click or not to click automatic selection of beautiful thumbnails from videos"

Connected through hyperedge HE-5 (induced by source paper "rethinking the evaluation of video summaries"):
  with members:
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "real time video highlights for yahoo esports"
    - "summary transfer exemplar based subset selection for video summarization"
    - "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-6 (induced by source paper "phd gifs personalized highlight detection for automatic gif creation"):
  with members:
    - "a deep ranking model for spatio temporal highlight detection from a 360 video"
    - "predicting visual context for unsupervised event segmentation in continuous photo streams"
    - "self normalizing neural networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "ridiculously fast shot boundary detection with fully convolutional neural networks"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "query focused extractive video 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)