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 image virality"
  Abstract: Virality of online content on social networking websites is an important but esoteric phenomenon often studied in fields like marketing, psychology and data mining. In this paper we study viral images from a computer vision perspective. We introduce three new image datasets from Reddit1 and define a virality score using Reddit metadata. We train classifiers with state-of-the-art image features to predict virality of individual images, relative virality in pairs of images, and the dominant topic of a viral image. We also compare machine performance to human performance on these tasks. We find that computers perform poorly with low level features, and high level information is critical for predicting virality. We encode semantic information through relative attributes. We identify the 5 key visual attributes that correlate with virality. We create an attribute-based characterization of images that can predict relative virality with 68.10% accuracy (SVM+Deep Relative Attributes) -better than humans at 60.12%. Finally, we study how human prediction of image virality varies with different “contexts” in which the images are viewed, such as the influence of neighbouring images, images recently viewed, as well as the image title or caption. This work is a first step in understanding the complex but important phenomenon of image virality. Our datasets and annotations will be made publicly available.

Connected through hyperedge HE-1 (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-2 (induced by source paper "intrinsic image popularity assessment"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "how to become instagram famous post popularity prediction with dual attention"
    - "pay attention to virality understanding popularity of social media videos with the attention mechanism"
    - "sequential prediction of social media popularity with deep temporal context networks"
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "modeling image virality with pairwise spatial transformer networks"
    - "time matters multi scale temporalization of social media popularity"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"

Connected through hyperedge HE-3 (induced by source paper "memesequencer sparse matching for embedding image macros"):
  with members:
    - "modeling image virality with pairwise spatial transformer networks"
    - "information evolution in social networks"
    - "do cascades recur"
    - "a corpus and evaluation framework for deeper understanding of commonsense stories"
    - "end to end localization and ranking for relative attributes"
    - "tensorflow a system for large scale machine learning"
    - "skip thought vectors"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "totally looks like how humans compare compared to machines"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "modeling human categorization of natural images using deep feature representations"
    - "recent advance in content based image retrieval a literature survey"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "adapting deep network features to capture psychological representations"
    - "understanding and mapping natural beauty"
    - "densely connected convolutional networks"
    - "deep learning in neural networks"

Connected through hyperedge HE-5 (induced by source paper "compare and contrast learning prominent visual differences"):
  with members:
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "fashion forward forecasting visual style in fashion"
    - "robust subjective visual property prediction from crowdsourced pairwise labels"
    - "shallow and deep convolutional networks for saliency prediction"
    - "end to end localization and ranking for relative attributes"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "generation and comprehension of unambiguous object descriptions"
    - "attribute2image conditional image generation from visual attributes"

Connected through hyperedge HE-6 (induced by source paper "pay attention to virality understanding popularity of social media videos with the attention mechanism"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "recurrent neural networks for online video popularity prediction"
    - "viraliency pooling local virality"
    - "shallow reading with deep learning predicting popularity of online content using only its title"
    - "predicting popularity of online videos using support vector regression"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"):
  with members:
    - "time matters multi scale temporalization of social media popularity"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "item popularity prediction in e commerce using image quality feature vectors"
    - "beyond exchangeability the chinese voting process"
    - "learning distributed representations of sentences from unlabelled data"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "deep residual learning for image recognition"
    - "an image is worth more than a thousand favorites surfacing the hidden beauty of flickr pictures"

Connected through hyperedge HE-8 (induced by source paper "who will share my image predicting the content diffusion path in online social networks"):
  with members:
    - "predicting the popularity of github repositories"
    - "node2vec scalable feature learning for networks"
    - "densecap fully convolutional localization networks for dense captioning"
    - "improved semantic representations from tree structured long short term memory networks"
    - "top down tree long short term memory networks"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "can cascades be predicted"

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.SI(Social and Information Networks)