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: "assessing vaccination sentiments with online social media implications for infectious disease dynamics and control"
  Abstract: There is great interest in the dynamics of health behaviors in social networks and how they affect collective public health outcomes, but measuring population health behaviors over time and space requires substantial resources. Here, we use publicly available data from 101,853 users of online social media collected over a time period of almost six months to measure the spatio-temporal sentiment towards a new vaccine. We validated our approach by identifying a strong correlation between sentiments expressed online and CDC-estimated vaccination rates by region. Analysis of the network of opinionated users showed that information flows more often between users who share the same sentiments - and less often between users who do not share the same sentiments - than expected by chance alone. We also found that most communities are dominated by either positive or negative sentiments towards the novel vaccine. Simulations of infectious disease transmission show that if clusters of negative vaccine sentiments lead to clusters of unprotected individuals, the likelihood of disease outbreaks is greatly increased. Online social media provide unprecedented access to data allowing for inexpensive and efficient tools to identify target areas for intervention efforts and to evaluate their effectiveness.

Connected through hyperedge HE-1 (induced by source paper "mining social media data for biomedical signals and health related behavior"):
  with members:
    - "singular value decomposition and neural networks"
    - "anatomy of an online misinformation network"
    - "human sexual cycles are driven by culture and match collective moods"
    - "limited individual attention and online virality of low quality information"
    - "quantified self meets social media sharing of weight updates on twitter"
    - "the rise of social bots"
    - "learning deep structure preserving image text embeddings"
    - "insights from machine learned diet success prediction"

Connected through hyperedge HE-2 (induced by source paper "modeling spatiotemporal factors associated with sentiment on twitter synthesis and suggestions for improving the identification of localized deviations"):
  with members:
    - "the happiness paradox your friends are happier than you"
    - "a survey of location inference techniques on twitter"
    - "what a nasty day exploring mood weather relationship from twitter"
    - "home location identification of twitter users"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "comparing and combining sentiment analysis methods"
    - "the dynamics of health behavior sentiments on a large online social network"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-3 (induced by source paper "evidence of distrust and disorientation towards immunization on online social media after contrasting political communication on vaccines results from an analysis of twitter data in italy"):
  with members:
    - "polarization of the vaccination debate on facebook"
    - "science vs conspiracy collective narratives in the age of misinformation"
    - "emotional dynamics in the age of misinformation"
    - "a planetary nervous system for social mining and collective awareness"
    - "machine learning in automated text categorization"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-4 (induced by source paper "did you really just have a heart attack towards robust detection of personal health mentions in social media"):
  with members:
    - "semi supervised approach to monitoring clinical depressive symptoms in social media"
    - "are word embedding based features useful for sarcasm detection"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "bag of tricks for efficient text classification"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "crowdbreaks tracking health trends using public social media data and crowdsourcing"):
  with members:
    - "leveraging large amounts of weakly supervised data for multi language sentiment classification"
    - "active discriminative word embedding learning"
    - "bag of tricks for efficient text classification"
    - "imagenet large scale visual recognition challenge"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "how well can machine learning predict demographics of social media users"):
  with members:
    - "demographics in social media data for public health research does it matter"
    - "predicting demographics of high resolution geographies with geotagged tweets"
    - "gender inference using statistical name characteristics in twitter"
    - "probabilistic inference of twitter users age based on what they follow"
    - "gender prediction in social media"
    - "gender identity and lexical variation in social media"
    - "inferring gender of a twitter user using celebrities it follows"

Connected through hyperedge HE-7 (induced by source paper "demographics in social media data for public health research does it matter"):
  with members:
    - "how well can machine learning predict demographics of social media users"
    - "gender inference using statistical name characteristics in twitter"
    - "linear and order statistics combiners for pattern classification"

Connected through hyperedge HE-8 (induced by source paper "nrc canada building the state of the art in sentiment analysis of tweets"):
  with members:
    - "tracking sentiment in mail how genders differ on emotional axes"
    - "thumbs up sentiment classification using machine learning techniques"

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)