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: "modeling opinion dynamics in diffusion networks"
  Abstract: Social media and social networking sites have become a global pinboard for exposition and discussion of news, topics, and ideas, where social media users increasingly form their opinion about a particular topic by learning information about it from her peers. In this context, whenever a user posts a message about a topic, we observe a noisy estimate of her current opinion about it but the influence the user may have on other users' opinions is hidden. In this paper, we introduce a probabilistic modeling framework of opinion dynamics, which allows the underlying opinion of a user to be modulated by those expressed by her neighbors over time. We then identify a set of conditions under which users' opinions converge to a steady state, find a linear relation between the initial opinions and the opinions in the steady state, and develop an efficient estimation method to fit the parameters of the model from historical fine-grained opinion and information diffusion event data. Experiments on data gathered from Twitter, Reddit and Amazon show that our model provides a good fit to the data and more accurate predictions than alternatives.

Connected through hyperedge HE-1 (induced by source paper "multistage campaigning in social networks"):
  with members:
    - "campaigning in heterogeneous social networks optimal control of si information epidemics"
    - "steering opinion dynamics in information diffusion networks"
    - "learning granger causality for hawkes processes"
    - "smart broadcasting do you want to be seen"
    - "influence prediction for continuous time information propagation on networks"
    - "learning and forecasting opinion dynamics in social networks"
    - "shaping social activity by incentivizing users"
    - "modeling adoption and usage of competing products"

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)