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: "a deep siamese network for scene detection in broadcast videos"
  Abstract: We present a model that automatically divides broadcast videos into coherent scenes by learning a distance measure between shots. Experiments are performed to demonstrate the effectiveness of our approach by comparing our algorithm against recent proposals for automatic scene segmentation. We also propose an improved performance measure that aims to reduce the gap between numerical evaluation and expected results, and propose and release a new benchmark dataset.

Connected through hyperedge HE-1 (induced by source paper "cricket stroke extraction towards creation of a large scale cricket actions dataset"):
  with members:
    - "the kinetics human action video dataset"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "temporal segment networks towards good practices for deep action recognition"
    - "youtube 8m a large scale video classification benchmark"
    - "tubelets unsupervised action proposals from spatiotemporal super voxels"
    - "end to end learning of action detection from frame glimpses in videos"
    - "beyond short snippets deep networks for video classification"

Connected through hyperedge HE-2 (induced by source paper "hierarchical boundary aware neural encoder for video captioning"):
  with members:
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "temporal segment networks towards good practices for deep action recognition"
    - "improving lstm based video description with linguistic knowledge mined from text"
    - "hierarchical multiscale recurrent neural networks"
    - "jointly modeling embedding and translation to bridge video and language"
    - "the long short story of movie description"
    - "a dataset for movie description"
    - "beyond short snippets deep networks for video classification"

Connected through hyperedge HE-3 (induced by source paper "recognizing and presenting the storytelling video structure with deep multimodal networks"):
  with members:
    - "videostory embeddings recognize events when examples are scarce"
    - "a data driven approach for tag refinement and localization in web videos"
    - "understanding deep image representations by inverting them"
    - "very deep convolutional networks for large scale image recognition"
    - "hypercolumns for object segmentation and fine grained localization"
    - "imagenet large scale visual recognition challenge"
    - "visualizing and understanding convolutional networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "scene driven retrieval in edited videos using aesthetic and semantic deep features"):
  with members:
    - "a data driven approach for tag refinement and localization in web videos"
    - "understanding deep image representations by inverting them"
    - "very deep convolutional networks for large scale image recognition"
    - "hypercolumns for object segmentation and fine grained localization"
    - "imagenet large scale visual recognition challenge"
    - "visualizing and understanding convolutional networks"
    - "distributed representations of words and phrases and their compositionality"

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)