human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "to click or not to click automatic selection of beautiful thumbnails from videos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "image to video domain adaptation using web supervision")
  Member "video summarization with long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "video storytelling textual summaries for events")
  Member "query focused extractive video summarization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convolutional hierarchical attention network for query focused video summarization")
  Member "video2gif automatic generation of animated gifs from video":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "talk2nav long range vision and language navigation with dual attention and spatial memory")
  Member "a data driven approach for tag refinement and localization in web videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "layout induced video representation for recognizing agent in place actions")
  Member "tagbook a semantic video representation without supervision for event detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic semantic retrieval for surveillance videos with activity graphs")
  Member "objects2action classifying and localizing actions without any video example":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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)