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 focused extractive video summarization"
  Abstract: Video data is explosively growing. As a result of the "big video data", intelligent algorithms for automatic video summarization have re-emerged as a pressing need. We develop a probabilistic model, Sequential and Hierarchical Determinantal Point Process (SH-DPP), for query-focused extractive video summarization. Given a user query and a long video sequence, our algorithm returns a summary by selecting key shots from the video. The decision to include a shot in the summary depends on the shot's relevance to the user query and importance in the context of the video, jointly. We verify our approach on two densely annotated video datasets. The query-focused video summarization is particularly useful for search engines, e.g., to display snippets of videos.

Connected through hyperedge HE-1 (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"
    - "query adaptive video summarization via quality aware relevance estimation"
    - "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"
    - "video summarization using deep semantic features"

Connected through hyperedge HE-2 (induced by source paper "multi modal deep analysis for multimedia"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "better and faster knowledge transfer from multiple self supervised learning tasks via graph distillation for video classification"
    - "visual question reasoning on general dependency tree"
    - "learning conditioned graph structures for interpretable visual question answering"
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-3 (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-4 (induced by source paper "personalizing fast forward videos based on visual and textual features from social network"):
  with members:
    - "making a long story short a multi importance fast forwarding egocentric videos with the emphasis on relevant objects"
    - "a weighted sparse sampling and smoothing frame transition approach for semantic fast forward first person videos"
    - "ffnet video fast forwarding via reinforcement learning"
    - "video salient object detection via fully convolutional networks"
    - "egosampling wide view hyperlapse from egocentric videos"
    - "predicting visual features from text for image and video caption retrieval"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "query focused video summarization dataset evaluation and a memory network based approach"

Connected through hyperedge HE-5 (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"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "video summarization using deep semantic features"
    - "videoset video summary evaluation through text"

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"
    - "video summarization with attention based encoder decoder networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "collaborative summarization of topic related videos"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-7 (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"
    - "query adaptive video summarization via quality aware relevance estimation"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "improving sequential determinantal point processes for supervised video summarization"):
  with members:
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "sequence to sequence learning as beam search optimization"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "minimum risk training for neural machine translation"
    - "sequence level training with recurrent neural networks"
    - "sequence to sequence video to text"

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)