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: "automatic understanding of image and video advertisements"
  Abstract: There is more to images than their objective physical content: for example, advertisements are created to persuade a viewer to take a certain action. We propose the novel problem of automatic advertisement understanding. To enable research on this problem, we create two datasets: an image dataset of 64,832 image ads, and a video dataset of 3,477 ads. Our data contains rich annotations encompassing the topic and sentiment of the ads, questions and answers describing what actions the viewer is prompted to take and the reasoning that the ad presents to persuade the viewer ("What should I do according to this ad, and why should I do it?"), and symbolic references ads make (e.g. a dove symbolizes peace). We also analyze the most common persuasive strategies ads use, and the capabilities that computer vision systems should have to understand these strategies. We present baseline classification results for several prediction tasks, including automatically answering questions about the messages of the ads.

Connected through hyperedge HE-1 (induced by source paper "recommending themes for ad creative design via visual linguistic representations"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "visualbert a simple and performant baseline for vision and language"
    - "vl bert pre training of generic visual linguistic representations"
    - "advise symbolism and external knowledge for decoding advertisements"
    - "a deep relevance matching model for ad hoc retrieval"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-2 (induced by source paper "adversarial perceptual ad blocking meets adversarial machine learning"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "stateful detection of black box adversarial attacks"
    - "percival making in browser perceptual ad blocking practical with deep learning"
    - "who filters the filters understanding the growth usefulness and efficiency of crowdsourced ad blocking"
    - "sentinet detecting physical attacks against deep learning systems"
    - "yolov3 an incremental improvement"
    - "fooling ocr systems with adversarial text images"
    - "adversarially robust generalization requires more data"

Connected through hyperedge HE-3 (induced by source paper "beyond visual semantics exploring the role of scene text in image understanding"):
  with members:
    - "towards vqa models that can read"
    - "exploring hate speech detection in multimodal publications"
    - "relation aware graph attention network for visual question answering"
    - "equal but not the same understanding the implicit relationship between persuasive images and text"
    - "fots fast oriented text spotting with a unified network"
    - "textually enriched neural module networks for visual question answering"
    - "don t only feel read using scene text to understand advertisements"
    - "understanding visual ads by aligning symbols and objects using co attention"

Connected through hyperedge HE-4 (induced by source paper "percival making in browser perceptual ad blocking practical with deep learning"):
  with members:
    - "who filters the filters understanding the growth usefulness and efficiency of crowdsourced ad blocking"
    - "adversarial logit pairing"
    - "understanding visual ads by aligning symbols and objects using co attention"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial attacks on neural network policies"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "the future of ad blocking an analytical framework and new techniques"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "understanding categorizing and predicting semantic image text relations"):
  with members:
    - "equal but not the same understanding the implicit relationship between persuasive images and text"
    - "webly supervised joint embedding for cross modal image text retrieval"
    - "multimodal machine learning a survey and taxonomy"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "fluency guided cross lingual image captioning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-6 (induced by source paper "integrating text and image determining multimodal document intent in instagram posts"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "from recognition to cognition visual commonsense reasoning"
    - "equal but not the same understanding the implicit relationship between persuasive images and text"
    - "deep contextualized word representations"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "understanding visual ads by aligning symbols and objects using co attention"
    - "knowledge rich image gist understanding beyond literal meaning"
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"

Connected through hyperedge HE-7 (induced by source paper "look read and feel benchmarking ads understanding with multimodal multitask learning"):
  with members:
    - "multi label image recognition with graph convolutional networks"
    - "multimodal content analysis for effective advertisements on youtube"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "cnn rnn a unified framework for multi label image classification"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "localizing adverts in outdoor scenes"):
  with members:
    - "the alos dataset for advert localization in outdoor scenes"
    - "adnet a deep network for detecting adverts"
    - "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"

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)