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: "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
  Abstract: The content of today's social media is becoming more and more rich, increasingly mixing text, images, videos, and audio. It is an intriguing research question to model the interplay between these different modes in attracting user attention and engagement. But in order to pursue this study of multimodal content, we must also account for context: timing effects, community preferences, and social factors (e.g., which authors are already popular) also affect the amount of feedback and reaction that social-media posts receive. In this work, we separate out the influence of these non-content factors in several ways. First, we focus on ranking pairs of submissions posted to the same community in quick succession, e.g., within 30 seconds; this framing encourages models to focus on time-agnostic and community-specific content features. Within that setting, we determine the relative performance of author vs. content features. We find that victory usually belongs to "cats and captions," as visual and textual features together tend to outperform identity-based features. Moreover, our experiments show that when considered in isolation, simple unigram text features and deep neural network visual features yield the highest accuracy individually, and that the combination of the two modalities generally leads to the best accuracies overall.

Connected through hyperedge HE-1 (induced by source paper "the pushshift reddit dataset"):
  with members:
    - "firsthand opiates abuse on social media monitoring geospatial patterns of interest through a digital cohort"
    - "towards ethical content based detection of online influence campaigns"
    - "content removal as a moderation strategy compliance and other outcomes in the changemyview community"
    - "are all successful communities alike characterizing and predicting the success of online communities"
    - "political discussions in homogeneous and cross cutting communication spaces"
    - "modelling structure and predicting dynamics of discussion threads in online boards"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "who let the trolls out towards understanding state sponsored trolls"

Connected through hyperedge HE-2 (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"
    - "viraliency pooling local virality"

Connected through hyperedge HE-3 (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"
    - "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"
    - "predicting popularity of online videos using support vector regression"

Connected through hyperedge HE-4 (induced by source paper "the anatomy of reddit an overview of academic research"):
  with members:
    - "modelling structure and predicting dynamics of discussion threads in online boards"
    - "the aftermath of disbanding an online hateful community"
    - "tracing community genealogy how new communities emerge from the old"
    - "community interaction and conflict on the web"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "on the origins of memes by means of fringe web communities"
    - "conversation modeling on reddit using a graph structured lstm"
    - "identifying the social signals that drive online discussions a case study of reddit communities"

Connected through hyperedge HE-5 (induced by source paper "models for predicting community specific interest in news articles"):
  with members:
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"
    - "identifying the social signals that drive online discussions a case study of reddit communities"
    - "the impact of crowds on news engagement a reddit case study"
    - "characterizing the language of online communities and its relation to community reception"
    - "breaking the news first impressions matter on online news"
    - "leveraging joint interactions for credibility analysis in news communities"
    - "talking to the crowd what do people react to in online discussions"
    - "a bayesian approach for predicting the popularity of tweets"

Connected through hyperedge HE-6 (induced by source paper "caveat emptor computational social science large scale missing data in a widely published reddit corpus"):
  with members:
    - "the rise and fall of network stars analyzing 2 5 million graphs to reveal how high degree vertices emerge over time"
    - "a web of hate tackling hateful speech in online social spaces"
    - "the impact of crowds on news engagement a reddit case study"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "analyzing complex network user arrival patterns and their effect on network topologies"
    - "averaging gone wrong using time aware analyses to better understand behavior"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "what do vegans do in their spare time latent interest detection in multi community networks"

Connected through hyperedge HE-7 (induced by source paper "woman metal white vs man dress shorts combining social temporal and image signals to understand popularity of pinterest fashion boards"):
  with members:
    - "sequential prediction of social media popularity with deep temporal context networks"
    - "cultural diffusion and trends in facebook photographs"
    - "evolution of fashion brands on twitter and instagram"
    - "pinterest board recommendation for twitter users"
    - "densecap fully convolutional localization networks for dense captioning"
    - "social bootstrapping how pinterest and last fm social communities benefit by borrowing links from facebook"

Connected through hyperedge HE-8 (induced by source paper "identifying the social signals that drive online discussions a case study of reddit communities"):
  with members:
    - "the impact of crowds on news engagement a reddit case study"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "characterizing the language of online communities and its relation to community reception"
    - "trend of narratives in the age of misinformation"
    - "breaking the news first impressions matter on online news"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "talking to the crowd what do people react to in online discussions"
    - "how to ask for a favor a case study on the success of altruistic requests"

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)