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: "polarization of the vaccination debate on facebook"
  Abstract: Vaccine hesitancy has been recognized as a major global health threat. Having access to any type of information in social media has been suggested as a potential powerful influence factor to hesitancy. Recent studies in other fields than vaccination show that access to a wide amount of content through the Internet without intermediaries resolved into major segregation of the users in polarized groups. Users select the information adhering to theirs system of beliefs and tend to ignore dissenting information. In this paper we assess whether there is polarization in Social Media use in the field of vaccination. We perform a thorough quantitative analysis on Facebook analyzing 2.6M users interacting with 298.018 posts over a time span of seven years and 5 months. We used community detection algorithms to automatically detect the emergent communities from the users activity and to quantify the cohesiveness over time of the communities. Our findings show that content consumption about vaccines is dominated by the echo-chamber effect and that polarization increased over years. Communities emerge from the users consumption habits, i.e. the majority of users only consumes information in favor or against vaccines, not both. The existence of echo-chambers may explain why social-media campaigns providing accurate information may have limited reach, may be effective only in sub-groups and might even foment further polarization of opinions. The introduction of dissenting information into a sub-group is disregarded and can have a backfire effect, further reinforcing the existing opinions within the sub-group.

Connected through hyperedge HE-1 (induced by source paper "the covid 19 social media infodemic"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "polarization and fake news early warning of potential misinformation targets"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "modeling the infectiousness of twitter hashtags"
    - "debunking in a world of tribes"
    - "science vs conspiracy collective narratives in the age of misinformation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "the role of bot squads in the political propaganda on twitter"):
  with members:
    - "better safe than sorry an adversarial approach to improve social bot detection"
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "cashtag piggybacking uncovering spam and bot activity in stock microblogs on twitter"
    - "the spread of low credibility content by social bots"
    - "social fingerprinting detection of spambot groups through dna inspired behavioral modeling"
    - "spectrum based deep neural networks for fraud detection"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "holoscope topology and spike aware fraud detection"

Connected through hyperedge HE-3 (induced by source paper "human values and attitudes towards vaccination in social media"):
  with members:
    - "evaluation of biases in self reported demographic and psychometric information traditional versus facebook based surveys"
    - "predicting demographics moral foundations and human values from digital behaviours"
    - "graph embedding techniques applications and performance a survey"
    - "struc2vec learning node representations from structural identity"
    - "node2vec scalable feature learning for networks"
    - "latent space model for multi modal social data"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-4 (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:
    - "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"
    - "assessing vaccination sentiments with online social media implications for infectious disease dynamics and control"
    - "machine learning in automated text categorization"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-5 (induced by source paper "polarization and fake news early warning of potential misinformation targets"):
  with members:
    - "misinformation spreading on facebook"
    - "it s always april fools day on the difficulty of social network misinformation classification via propagation features"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "fake news detection on social media a data mining perspective"
    - "automatically identifying fake news in popular twitter threads"
    - "tweet acts a speech act classifier for twitter"
    - "debunking in a world of tribes"
    - "vews a wikipedia vandal early warning system"

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)