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: "videoset video summary evaluation through text"
  Abstract: In this paper we present VideoSET, a method for Video Summary Evaluation through Text that can evaluate how well a video summary is able to retain the semantic information contained in its original video. We observe that semantics is most easily expressed in words, and develop a text-based approach for the evaluation. Given a video summary, a text representation of the video summary is first generated, and an NLP-based metric is then used to measure its semantic distance to ground-truth text summaries written by humans. We show that our technique has higher agreement with human judgment than pixel-based distance metrics. We also release text annotations and ground-truth text summaries for a number of publicly available video datasets, for use by the computer vision community.

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 "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-3 (induced by source paper "neural language modeling with visual features"):
  with members:
    - "visually grounded situated learning in neural models"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "towards automatic learning of procedures from web instructional videos"
    - "localizing moments in video with natural language"
    - "the something something video database for learning and evaluating visual common sense"
    - "grounding of textual phrases in images by reconstruction"
    - "youtube 8m a large scale video classification benchmark"

Connected through hyperedge HE-4 (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"
    - "query focused extractive video summarization"
    - "video summarization using deep semantic features"

Connected through hyperedge HE-5 (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"
    - "query focused extractive video summarization"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "a framework towards domain specific video summarization"):
  with members:
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "a unified multi faceted video summarization system"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "query focused extractive video summarization"
    - "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 "localizing moments in video with natural language"):
  with members:
    - "unsupervised visual linguistic reference resolution in instructional videos"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"
    - "tgif qa toward spatio temporal reasoning in visual question answering"
    - "tall temporal activity localization via language query"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "modeling relationships in referential expressions with compositional modular networks"
    - "learning language visual embedding for movie understanding with natural language"

Connected through hyperedge HE-8 (induced by source paper "dense captioning events in videos"):
  with members:
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "a hierarchical approach for generating descriptive image paragraphs"
    - "learning joint representations of videos and sentences with web image search"
    - "embracing error to enable rapid crowdsourcing"
    - "dense captioning with joint inference and visual context"
    - "jointly modeling embedding and translation to bridge video and language"
    - "the long short story of movie description"

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)