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: "adnet a deep network for detecting adverts"
  Abstract: Online video advertising gives content providers the ability to deliver compelling content, reach a growing audience, and generate additional revenue from online media. Recently, advertising strategies are designed to look for original advert(s) in a video frame, and replacing them with new adverts. These strategies, popularly known as product placement or embedded marketing, greatly help the marketing agencies to reach out to a wider audience. However, in the existing literature, such detection of candidate frames in a video sequence for the purpose of advert integration, is done manually. In this paper, we propose a deep-learning architecture called ADNet, that automatically detects the presence of advertisements in video frames. Our approach is the first of its kind that automatically detects the presence of adverts in a video frame, and achieves state-of-the-art results on a public dataset.

Connected through hyperedge HE-1 (induced by source paper "identifying candidate spaces for advert implantation"):
  with members:
    - "the case dataset of candidate spaces for advert implantation"
    - "localizing adverts in outdoor scenes"
    - "an advert creation system for next gen publicity"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "the case dataset of candidate spaces for advert implantation"):
  with members:
    - "test a terminology extraction system for technology related terms"
    - "an advert creation system for next gen publicity"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "semantic understanding of scenes through the ade20k dataset"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "localizing adverts in outdoor scenes"):
  with members:
    - "the alos dataset for advert localization in outdoor scenes"
    - "automatic understanding of image and video advertisements"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "the alos dataset for advert localization in outdoor scenes"):
  with members:
    - "the case dataset of candidate spaces for advert implantation"
    - "an advert creation system for next gen publicity"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "test a terminology extraction system for technology related terms"):
  with members:
    - "an advert creation system for next gen publicity"
    - "bag of tricks for efficient text classification"

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.MM(Multimedia)