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: "understanding infographics through textual and visual tag prediction"
  Abstract: We introduce the problem of visual hashtag discovery for infographics: extracting visual elements from an infographic that are diagnostic of its topic. Given an infographic as input, our computational approach automatically outputs textual and visual elements predicted to be representative of the infographic content. Concretely, from a curated dataset of 29K large infographic images sampled across 26 categories and 391 tags, we present an automated two step approach. First, we extract the text from an infographic and use it to predict text tags indicative of the infographic content. And second, we use these predicted text tags as a supervisory signal to localize the most diagnostic visual elements from within the infographic i.e. visual hashtags. We report performances on a categorization and multi-label tag prediction problem and compare our proposed visual hashtags to human annotations.

Connected through hyperedge HE-1 (induced by source paper "towards automated infographic design deep learning based auto extraction of extensible timeline"):
  with members:
    - "synthetically trained icon proposals for parsing and summarizing infographics"
    - "vizml a machine learning approach to visualization recommendation"
    - "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"
    - "dvqa understanding data visualizations via question answering"
    - "focal loss for dense object detection"
    - "dynamic routing between capsules"
    - "learning visual importance for graphic designs and data visualizations"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "variational prototyping encoder one shot learning with prototypical images"):
  with members:
    - "prototypical networks for few shot learning"
    - "co domain embedding using deep quadruplet networks for unseen traffic sign recognition"
    - "open set logo detection and retrieval"
    - "a simple neural attentive meta learner"
    - "deep learning logo detection with data expansion by synthesising context"
    - "deep feature consistent variational autoencoder"
    - "image to image translation with conditional adversarial networks"
    - "matching networks for one shot learning"

Connected through hyperedge HE-3 (induced by source paper "text to viz automatic generation of infographics from proportion related natural language statements"):
  with members:
    - "synthetically trained icon proposals for parsing and summarizing infographics"
    - "datasite proactive visual data exploration with computation of insight based recommendations"
    - "vizml a machine learning approach to visualization recommendation"
    - "textual analogy parsing what s shared and what s compared among analogous facts"
    - "foresight recommending visual insights"
    - "convolutional neural network architectures for matching natural language sentences"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (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"
    - "advise symbolism and external knowledge for decoding advertisements"
    - "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-5 (induced by source paper "saliency prediction in the deep learning era successes limitations and future challenges"):
  with members:
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "deep visual attention prediction"
    - "charades ego a large scale dataset of paired third and first person videos"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "learning a saliency evaluation metric using crowdsourced perceptual judgments"
    - "deepvs a deep learning based video saliency prediction approach"

Connected through hyperedge HE-6 (induced by source paper "advise symbolism and external knowledge for decoding advertisements"):
  with members:
    - "sampling matters in deep embedding learning"
    - "image2song song retrieval via bridging image content and lyric words"
    - "inferring and executing programs for visual reasoning"
    - "in defense of the triplet loss for person re identification"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "context aware captions from context agnostic supervision"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "self supervised learning of visual features through embedding images into text topic spaces"

Connected through hyperedge HE-7 (induced by source paper "separating self expression and visual content in hashtag supervision"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "the devil is in the tails fine grained classification in the wild"
    - "learning from noisy large scale datasets with minimal supervision"
    - "information theoretical label embeddings for large scale image classification"
    - "learning visual n grams from web data"
    - "yfcc100m the new data in multimedia research"
    - "deep residual learning for image recognition"
    - "learning visual features from large weakly supervised data"

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)