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: "characterizing information diets of social media users"
  Abstract: With the widespread adoption of social media sites like Twitter and Facebook, there has been a shift in the way information is produced and consumed. Earlier, the only producers of information were traditional news organizations, which broadcast the same carefully-edited information to all consumers over mass media channels. Whereas, now, in online social media, any user can be a producer of information, and every user selects which other users she connects to, thereby choosing the information she consumes. Moreover, the personalized recommendations that most social media sites provide also contribute towards the information consumed by individual users. In this work, we define a concept of information diet -- which is the topical distribution of a given set of information items (e.g., tweets) -- to characterize the information produced and consumed by various types of users in the popular Twitter social media. At a high level, we find that (i) popular users mostly produce very specialized diets focusing on only a few topics; in fact, news organizations (e.g., NYTimes) produce much more focused diets on social media as compared to their mass media diets, (ii) most users' consumption diets are primarily focused towards one or two topics of their interest, and (iii) the personalized recommendations provided by Twitter help to mitigate some of the topical imbalances in the users' consumption diets, by adding information on diverse topics apart from the users' primary topics of interest.

Connected through hyperedge HE-1 (induced by source paper "vocabulary based method for quantifying controversy in social media"):
  with members:
    - "quantifying polarization on twitter the kavanaugh nomination"
    - "community interaction and conflict on the web"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "a distance measure for the analysis of polar opinion dynamics in social networks"
    - "using word embeddings in twitter election classification"
    - "reducing controversy by connecting opposing views"
    - "characterizing the language of online communities and its relation to community reception"

Connected through hyperedge HE-2 (induced by source paper "how often should i access my online social networks"):
  with members:
    - "age of information vs value of information scheduling for cellular networked control systems"
    - "performance analysis of online social platforms"
    - "minimizing the age of information in wireless networks with stochastic arrivals"
    - "biases in the facebook news feed a case study on the italian elections"
    - "fairness in online social network timelines measurements models and mechanism design"
    - "age optimal sampling and transmission scheduling in multi source systems"
    - "optimal status update for age of information minimization with an energy harvesting source"
    - "the age of information real time status updating by multiple sources"

Connected through hyperedge HE-3 (induced by source paper "fairness in online social network timelines measurements models and mechanism design"):
  with members:
    - "biases in the facebook news feed a case study on the italian elections"
    - "do diffusion protocols govern cascade growth"
    - "closing the ai knowledge gap"
    - "fairness of exposure in rankings"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "alpha fair coded caching"
    - "on fairness and calibration"
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"

Connected through hyperedge HE-4 (induced by source paper "the road to popularity the dilution of growing audience on twitter"):
  with members:
    - "distinguishing between topical and non topical information diffusion mechanisms in social media"
    - "u s religious landscape on twitter"

Connected through hyperedge HE-5 (induced by source paper "quantifying controversy in social media"):
  with members:
    - "controversy and sentiment in online news"
    - "partisan sharing facebook evidence and societal consequences"
    - "social determinants of content selection in the age of mis information"
    - "data portraits connecting people of opposing views"
    - "efficient community detection in large networks using content and links"
    - "modularity clustering is force directed layout"

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)