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 conditioned three player adversarial network for video summarization"
  Abstract: Video summarization plays an important role in video understanding by selecting key frames/shots. Traditionally, it aims to find the most representative and diverse contents in a video as short summaries. Recently, a more generalized task, query-conditioned video summarization, has been introduced, which takes user queries into consideration to learn more user-oriented summaries. In this paper, we propose a query-conditioned three-player generative adversarial network to tackle this challenge. The generator learns the joint representation of the user query and the video content, and the discriminator takes three pairs of query-conditioned summaries as the input to discriminate the real summary from a generated and a random one. A three-player loss is introduced for joint training of the generator and the discriminator, which forces the generator to learn better summary results, and avoids the generation of random trivial summaries. Experiments on a recently proposed query-conditioned video summarization benchmark dataset show the efficiency and efficacy of our proposed method.

Connected through hyperedge HE-1 (induced by source paper "convolutional hierarchical attention network for query focused video summarization"):
  with members:
    - "viewpoint aware 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"
    - "query focused extractive video summarization"
    - "video summarization using deep semantic features"

Connected through hyperedge HE-2 (induced by source paper "video summarization by learning from unpaired data"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "how local is the local diversity reinforcing sequential determinantal point processes with dynamic ground sets for supervised video summarization"
    - "improving sequential determinantal point processes for supervised video summarization"
    - "video summarization using fully convolutional sequence networks"
    - "video summarisation by classification with deep reinforcement learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "collaborative summarization of topic related videos"

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)