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: "joint non negative matrix factorization for learning ideological leaning on twitter"
  Abstract: People are shifting from traditional news sources to online news at an incredibly fast rate. However, the technology behind online news consumption promotes content that confirms the users' existing point of view. This phenomenon has led to polarization of opinions and intolerance towards opposing views. Thus, a key problem is to model information filter bubbles on social media and design methods to eliminate them. In this paper, we use a machine-learning approach to learn a liberal-conservative ideology space on Twitter, and show how we can use the learned latent space to tackle the filter bubble problem. #R##N#We model the problem of learning the liberal-conservative ideology space of social media users and media sources as a constrained non-negative matrix-factorization problem. Our model incorporates the social-network structure and content-consumption information in a joint factorization problem with shared latent factors. We validate our model and solution on a real-world Twitter dataset consisting of controversial topics, and show that we are able to separate users by ideology with over 90% purity. When applied to media sources, our approach estimates ideology scores that are highly correlated (Pearson correlation 0.9) with ground-truth ideology scores. Finally, we demonstrate the utility of our model in real-world scenarios, by illustrating how the learned ideology latent space can be used to develop exploratory and interactive interfaces that can help users in diffusing their information filter bubble.

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"
    - "a distance measure for the analysis of polar opinion dynamics in social networks"
    - "characterizing information diets of social media users"
    - "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 "stop tracking me bro differential tracking of user demographics on hyper partisan websites"):
  with members:
    - "characterising third party cookie usage in the eu after gdpr"
    - "tweeting mps digital engagement between citizens and members of parliament in the uk"
    - "cookie synchronization everything you always wanted to know but were afraid to ask"
    - "no more chasing waterfalls a measurement study of the header bidding ad ecosystem"
    - "tracing information flows between ad exchanges using retargeted ads"
    - "talon an automated framework for cross device tracking detection"
    - "illuminating an ecosystem of partisan websites"
    - "whotracks me shedding light on the opaque world of online tracking"

Connected through hyperedge HE-3 (induced by source paper "ideotrace a framework for ideology tracing with a case study on the 2016 u s presidential election"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "red bots do it better comparative analysis of social bot partisan behavior"
    - "predicting factuality of reporting and bias of news media sources"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "impact of social media posts in real life violence a case study in bangladesh"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "who falls for online political manipulation"
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"

Connected through hyperedge HE-4 (induced by source paper "reconciliation k median clustering with non polarized representatives"):
  with members:
    - "constant factor approximation for ordered k median"
    - "minimizing polarization and disagreement in social networks"
    - "balancing information exposure in social networks"
    - "reducing controversy by connecting opposing views"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "bi factor approximation algorithms for hard capacitated k median problems"
    - "approximating k median via pseudo approximation"

Connected through hyperedge HE-5 (induced by source paper "neural based statement classification for biased language"):
  with members:
    - "selection bias in news coverage learning it fighting it"
    - "finding news citations for wikipedia"
    - "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
    - "how much is wikipedia lagging behind news"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "a dynamic embedding model of the media landscape"):
  with members:
    - "selection bias in news coverage learning it fighting it"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "two tales of the world comparison of widely used world news datasets gdelt and eventregistry"
    - "vista a visually socially and temporally aware model for artistic recommendation"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-7 (induced by source paper "maximizing the diversity of exposure in a social network"):
  with members:
    - "finding low tension communities"
    - "minimizing polarization and disagreement in social networks"
    - "balancing information exposure in social networks"
    - "revenue maximization in incentivized social advertising"
    - "reducing controversy by connecting opposing views"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "modeling adoption and usage of competing products"

Connected through hyperedge HE-8 (induced by source paper "tell me something my friends do not know diversity maximization in social networks"):
  with members:
    - "minimizing polarization and disagreement in social networks"
    - "balancing information exposure in social networks"
    - "users polarization on facebook and youtube"
    - "reducing controversy by connecting opposing views"
    - "quantifying controversy in social media"

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)