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: "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.

Connected through hyperedge HE-1 (induced by source paper "summarizing videos with attention"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "amnet memorability estimation with attention"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "attention is all you need"
    - "video summarization with attention based encoder decoder networks"
    - "convolutional sequence to sequence learning"
    - "a structured self attentive sentence embedding"
    - "reflections on shannon information in search of a natural information entropy for images"

Connected through hyperedge HE-2 (induced by source paper "video summarisation by classification with deep reinforcement learning"):
  with members:
    - "dilated temporal relational adversarial network for generic video summarization"
    - "learning deep sketch abstraction"
    - "ffnet video fast forwarding via reinforcement learning"
    - "discovering visual concept structure with sparse and incomplete tags"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "actor critic sequence training for image captioning"
    - "video captioning via hierarchical reinforcement learning"
    - "learning policies for adaptive tracking with deep feature cascades"

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)