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: "equal but not the same understanding the implicit relationship between persuasive images and text"
  Abstract: Images and text in advertisements interact in complex, non-literal ways. The two channels are usually complementary, with each channel telling a different part of the story. Current approaches, such as image captioning methods, only examine literal, redundant relationships, where image and text show exactly the same content. To understand more complex relationships, we first collect a dataset of advertisement interpretations for whether the image and slogan in the same visual advertisement form a parallel (conveying the same message without literally saying the same thing) or non-parallel relationship, with the help of workers recruited on Amazon Mechanical Turk. We develop a variety of features that capture the creativity of images and the specificity or ambiguity of text, as well as methods that analyze the semantics within and across channels. We show that our method outperforms standard image-text alignment approaches on predicting the parallel/non-parallel relationship between image and text.

Connected through hyperedge HE-1 (induced by source paper "deep multi modal sets"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "supervised multimodal bitransformers for classifying images and text"
    - "mfas multimodal fusion architecture search"
    - "roberta a robustly optimized bert pretraining approach"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "deep sets"
    - "exploring the limits of weakly supervised pretraining"
    - "rosetta large scale system for text detection and recognition in images"

Connected through hyperedge HE-2 (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"
    - "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"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"

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"
    - "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"
    - "incorporating syntactic and semantic information in word embeddings using graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "understanding categorizing and predicting semantic image text relations"):
  with members:
    - "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"
    - "automatic understanding of image and video advertisements"
    - "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-5 (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"
    - "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"
    - "viraliency pooling local virality"

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)