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 "convolutional hierarchical attention network for query focused video summarization"):
  Abstract: Previous approaches for video summarization mainly concentrate on finding the most diverse and representative visual contents as video summary without considering the user's preference. This paper addresses the task of query-focused video summarization, which takes user's query and a long video as inputs and aims to generate a query-focused video summary. In this paper, we consider the task as a problem of computing similarity between video shots and query. To this end, we propose a method, named Convolutional Hierarchical Attention Network (CHAN), which consists of two parts: feature encoding network and query-relevance computing module. In the encoding network, we employ a convolutional network with local self-attention mechanism and query-aware global attention mechanism to learns visual information of each shot. The encoded features will be sent to query-relevance computing module to generate queryfocused video summary. Extensive experiments on the benchmark dataset demonstrate the competitive performance and show the effectiveness of our approach.

Includes members:
  Member "viewpoint aware video summarization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dilated temporal relational adversarial network for generic video summarization")
  Member "query conditioned three player adversarial network for video summarization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "video summarization by learning from unpaired data")
  Member "video summarization using fully convolutional sequence networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "summarynet a multi stage deep learning model for automatic video summarisation")
  Member "query adaptive video summarization via quality aware relevance estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "video storytelling textual summaries for events")
  Member "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "summarynet a multi stage deep learning model for automatic video summarisation")
  Member "query focused video summarization dataset evaluation and a memory network based approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi modal deep analysis for multimedia")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "query focused extractive video summarization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi modal deep analysis for multimedia")

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)