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 "learning video summarization using unpaired data"):
  Abstract: We consider the problem of video summarization. Given an input raw video, the goal is to select a small subset of key frames from the input video to create a shorter summary video that best describes the content of the original video. Most of the current state-of-the-art video summarization approaches use supervised learning and require training data in the form of paired videos. Each pair consists of a raw input video and a ground-truth summary video curated by human annotators. However, it is difficult to create such paired training data. To address this limitation, we propose a novel formulation to learn video summarization from unpaired data. Our method only requires training data in the form of two sets of videos: a set of raw videos ($V$) and another set of summary videos ($S$). We do not require the correspondence between these two sets of videos. We argue that this type of data is much easier to collect. We present an approach that learns to generate summary videos from such unpaired data. We achieve this by learning a mapping function $F : V \rightarrow S $ which tries to make the distribution of generated summary videos from $F(V)$ similar to the distribution of $S$ while enforcing a high visual diversity constraint on $F(V)$. Experimental results on two benchmark datasets show that our proposed approach significantly outperforms other alternative methods.

Includes members:
  Member "video summarization using fully convolutional sequence networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "summarynet a multi stage deep learning model for automatic video summarisation")
  Member "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "summarynet a multi stage deep learning model for automatic video summarisation")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "collaborative summarization of topic related videos":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning for task driven video summarization")
  Member "energy based generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "video summarization with long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "video storytelling textual summaries for events")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)