human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "the robustness of democratic consensus"):
  Abstract: In linear models of consensus dynamics, the state of the various agents converges to a value which is a convex combination of the agents' initial states. We call it democratic if in the large scale limit (number of agents going to infinity) the vector of convex weights converges to 0 uniformly.Democracy is a relevant property which naturally shows up when we deal with opinion dynamic models and cooperative algorithms such as consensus over a network: it says that each agent's measure/opinion is going to play a negligible role in the asymptotic behavior of the global system. It can be seen as a relaxation of average consensus, where all agents have exactly the same weight in the final value, which becomes negligible for a large number of agents.We prove that starting from consensus models described by time-reversible stochastic matrices, under some mild technical assumptions, democracy is preserved when we perturb the linear dynamics in finitely many vertices. We want to stress that the local perturbation in general breaks the time-reversibility of the stochastic matrices. The main technical assumption needed in our result is the irreducibility of the large scale limit stochastic matrix, i.e. strong connectedness of the limit network of agents, and we show with an example that this assumption is indeed required.

Includes members:
  Member "pagerank optimization by edge selection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems")
  Member "gossips and prejudices ergodic randomized dynamics in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning political dna in the italian senate")
  Member "opinion fluctuations and disagreement in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "shifting opinions in a social network through leader selection")
  Member "distributed randomized algorithms for the pagerank computation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a tutorial on modeling and analysis of dynamic social networks part i")

Question: Please tell me which class the source paper 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.SY(Systems and Control)