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: "opinion maximization in social networks"
  Abstract: The process of opinion formation through synthesis and contrast of different viewpoints has been the subject of many studies in economics and social sciences. Today, this process manifests itself also in online social networks and social media. The key characteristic of successful promotion campaigns is that they take into consideration such opinion-formation dynamics in order to create a overall favorable opinion about a specific information item, such as a person, a product, or an idea. #R##N#In this paper, we adopt a well-established model for social-opinion dynamics and formalize the campaign-design problem as the problem of identifying a set of target individuals whose positive opinion about an information item will maximize the overall positive opinion for the item in the social network. We call this problem CAMPAIGN. We study the complexity of the CAMPAIGN problem, and design algorithms for solving it. Our experiments on real data demonstrate the efficiency and practical utility of our algorithms.

Connected through hyperedge HE-1 (induced by source paper "angry birds flock together aggression propagation on social media"):
  with members:
    - "detecting cyberbullying and cyberaggression in social media"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "the social world of content abusers in community question answering"

Connected through hyperedge HE-2 (induced by source paper "adversarial perturbations of opinion dynamics in networks"):
  with members:
    - "minimizing polarization and disagreement in social networks"
    - "how bad is forming your own opinion"
    - "twice ramanujan sparsifiers"
    - "spectral sparsification of graphs"
    - "max cut and the smallest eigenvalue"

Connected through hyperedge HE-3 (induced by source paper "network disruption maximizing disagreement and polarization in social networks"):
  with members:
    - "adversarial perturbations of opinion dynamics in networks"
    - "analysis of strategy and spread of russia sponsored content in the us in 2017"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "minimizing polarization and disagreement in social networks"
    - "some like it hoax automated fake news detection in social networks"

Connected through hyperedge HE-4 (induced by source paper "continuous influence based community partition for social networks"):
  with members:
    - "maximizing welfare in social networks under a utility driven influence diffusion model"
    - "influence maximization near optimal time complexity meets practical efficiency"

Connected through hyperedge HE-5 (induced by source paper "shifting opinions in a social network through leader selection"):
  with members:
    - "current flow group closeness centrality for complex networks"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "opinion dynamics with stubborn agents"
    - "minimizing polarization and disagreement in social networks"
    - "from local averaging to emergent global behaviors the fundamental role of network interconnections"
    - "an efficient parallel solver for sdd linear systems"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "minimizing convergence error in multi agent systems via leader selection a supermodular optimization approach"

Connected through hyperedge HE-6 (induced by source paper "disagreement and polarization in two party social networks"):
  with members:
    - "shifting opinions in a social network through leader selection"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "opinion dynamics with stubborn agents"
    - "minimizing polarization and disagreement in social networks"
    - "from local averaging to emergent global behaviors the fundamental role of network interconnections"
    - "opinion fluctuations and disagreement in social networks"
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-7 (induced by source paper "understanding filter bubbles and polarization in social networks"):
  with members:
    - "opinion dynamics with varying susceptibility to persuasion"
    - "eigenvector computation and community detection in asynchronous gossip models"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "maximizing the diversity of exposure in a social network"
    - "find your place simple distributed algorithms for community detection"
    - "biased assimilation homophily and the dynamics of polarization"
    - "how bad is forming your own opinion"

Connected through hyperedge HE-8 (induced by source paper "smart information spreading for opinion maximization in social networks"):
  with members:
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "maximizing social influence in nearly optimal time"
    - "irie scalable and robust influence maximization in social networks"

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)