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: "video summarization in a multi view camera network"
  Abstract: While most existing video summarization approaches aim to extract an informative summary of a single video, we propose a novel framework for summarizing multi-view videos by exploiting both intra- and inter-view content correlations in a joint embedding space. We learn the embedding by minimizing an objective function that has two terms: one due to intra-view correlations and another due to inter-view correlations across the multiple views. The solution can be obtained directly by solving one Eigen-value problem that is linear in the number of multi-view videos. We then employ a sparse representative selection approach over the learned embedding space to summarize the multi-view videos. Experimental results on several benchmark datasets demonstrate that our proposed approach clearly outperforms the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "multi stream dynamic video summarization"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "video summarization via actionness ranking"
    - "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"
    - "diversity aware multi video summarization"
    - "identifying first person camera wearers in third person videos"
    - "video summarization with attention based encoder decoder networks"
    - "query focused video summarization dataset evaluation and a memory network based approach"

Connected through hyperedge HE-2 (induced by source paper "multi view surveillance video summarization via joint embedding and sparse optimization"):
  with members:
    - "collaborative summarization of topic related videos"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "learning from multiple sources for video summarisation"
    - "correntropy induced l2 graph for robust subspace clustering"
    - "active object localization with deep reinforcement learning"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "diversity aware multi video summarization"):
  with members:
    - "collaborative summarization of topic related videos"
    - "summary transfer exemplar based subset selection for video summarization"
    - "deep multimodal semantic embeddings for speech and images"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "imagenet large scale visual recognition challenge"
    - "dissimilarity based sparse subset selection"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-4 (induced by source paper "collaborative summarization of topic related videos"):
  with members:
    - "video summarization with long short term memory"
    - "query focused extractive video summarization"
    - "correntropy induced l2 graph for robust subspace clustering"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "two stream convolutional networks for action recognition in 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)