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: "balancing information exposure in social networks"
  Abstract: Social media has brought a revolution on how people are consuming news. Beyond the undoubtedly large number of advantages brought by social-media platforms, a point of criticism has been the creation of echo chambers and filter bubbles, caused by social homophily and algorithmic personalization. #R##N#In this paper we address the problem of balancing the information exposure in a social network. We assume that two opposing campaigns (or viewpoints) are present in the network, and that network nodes have different preferences towards these campaigns. Our goal is to find two sets of nodes to employ in the respective campaigns, so that the overall information exposure for the two campaigns is balanced. We formally define the problem, characterize its hardness, develop approximation algorithms, and present experimental evaluation results. #R##N#Our model is inspired by the literature on influence maximization, but we offer significant novelties. First, balance of information exposure is modeled by a symmetric difference function, which is neither monotone nor submodular, and thus, not amenable to existing approaches. Second, while previous papers consider a setting with selfish agents and provide bounds on best response strategies (i.e., move of the last player), we consider a setting with a centralized agent and provide bounds for a global objective function.

Connected through hyperedge HE-1 (induced by source paper "combating fake news a survey on identification and mitigation techniques"):
  with members:
    - "the web of false information rumors fake news hoaxes clickbait and various other shenanigans"
    - "adversarial classification on social networks"
    - "all in one multi task learning for rumour verification"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "fever a large scale dataset for fact extraction and verification"
    - "detection and resolution of rumours in social media a survey"
    - "me my echo chamber and i introspection on social media polarization"
    - "fake news detection in social networks via crowd signals"

Connected through hyperedge HE-2 (induced by source paper "on multi cascade influence maximization model hardness and algorithmic framework"):
  with members:
    - "on misinformation containment in online social networks"
    - "distributed rumor blocking with multiple positive cascades"
    - "an efficient randomized algorithm for rumor blocking in online social networks"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "correlated cascades compete or cooperate"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"

Connected through hyperedge HE-3 (induced by source paper "diversifying seeds and audience in social influence maximization"):
  with members:
    - "tell me something my friends do not know diversity maximization in social networks"
    - "maximizing the diversity of exposure in a social network"
    - "node2vec scalable feature learning for networks"
    - "robust influence maximization"
    - "boosting information spread an algorithmic approach"
    - "line large scale information network embedding"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-4 (induced by source paper "balancing spreads of influence in a social network"):
  with members:
    - "coverage centrality maximization in undirected networks"
    - "tell me something my friends do not know diversity maximization in social networks"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "maximizing the diversity of exposure in a social network"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "the densest k subhypergraph problem"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-5 (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"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "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-6 (induced by source paper "cascade size distributions and why they matter"):
  with members:
    - "fake news detection in social networks via crowd signals"
    - "mitigating overexposure in viral marketing"
    - "catch em all locating multiple diffusion sources in networks with partial observations"
    - "optimal deployment of resources for maximizing impact in spreading processes"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "back to the past source identification in diffusion networks from partially observed cascades"

Connected through hyperedge HE-7 (induced by source paper "influence maximization via representation learning"):
  with members:
    - "topological recurrent neural network for diffusion prediction"
    - "node2vec scalable feature learning for networks"
    - "deepcas an end to end predictor of information cascades"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "a data based approach to social influence maximization"

Connected through hyperedge HE-8 (induced by source paper "a geometric model of opinion polarization"):
  with members:
    - "on the inevitability of online echo chambers"
    - "novel multidimensional models of opinion dynamics in social networks"
    - "reducing controversy by connecting opposing views"
    - "the evolution of beliefs over signed social networks"
    - "biased assimilation homophily and the dynamics of polarization"

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)