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: "advise symbolism and external knowledge for decoding advertisements"
  Abstract: In order to convey the most content in their limited space, advertisements embed references to outside knowledge via symbolism. For example, a motorcycle stands for adventure (a positive property the ad wants associated with the product being sold), and a gun stands for danger (a negative property to dissuade viewers from undesirable behaviors). We show how to use symbolic references to better understand the meaning of an ad. We further show how anchoring ad understanding in general-purpose object recognition and image captioning can further improve results. We formulate the ad understanding task as matching the ad image to human-generated statements that describe the action that the ad prompts and the rationale it provides for this action. We greatly outperform the state of the art in this task. We also show additional applications of our learned representations for ranking the slogans of ads, and clustering ads according to their topic.

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"
    - "automatic understanding of image and video 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 "polysemous visual semantic embedding for cross modal retrieval"):
  with members:
    - "on the convergence of adam and beyond"
    - "image2gif generating cinemagraphs using recurrent deep q networks"
    - "stacked cross attention for image text matching"
    - "cross modal retrieval with implicit concept association"
    - "modeling uncertainty with hedged instance embedding"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "attention is all you need"
    - "learning robust visual semantic embeddings"

Connected through hyperedge HE-3 (induced by source paper "cap2det learning to amplify weak caption supervision for object detection"):
  with members:
    - "min entropy latent model for weakly supervised object detection"
    - "equal but not the same understanding the implicit relationship between persuasive images and text"
    - "neural baby talk"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "deep learning for generic object detection a survey"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "artistic object recognition by unsupervised style adaptation"

Connected through hyperedge HE-4 (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-5 (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-6 (induced by source paper "huse hierarchical universal semantic embeddings"):
  with members:
    - "adversarial representation learning for text to image matching"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "hierarchy based image embeddings for semantic image retrieval"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "vse improving visual semantic embeddings with hard negatives"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"

Connected through hyperedge HE-7 (induced by source paper "serif or sans visual font analytics on book covers and online advertisements"):
  with members:
    - "east an efficient and accurate scene text detector"
    - "understanding infographics through textual and visual tag prediction"
    - "automatic understanding of image and video advertisements"
    - "awesome typography statistics based text effects transfer"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "deepfont identify your font from an image"

Connected through hyperedge HE-8 (induced by source paper "learning to discover and localize visual objects with open vocabulary"):
  with members:
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "deep learning for generic object detection a survey"
    - "learning to separate object sounds by watching unlabeled video"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "objects that sound"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "bottom up and top down attention for image captioning and visual question answering"

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)