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: "on the non progressive spread of influence through social networks"
  Abstract: The spread of influence in social networks is studied in two main categories: the progressive model and the non-progressive model (see e.g. the seminal work of Kempe, Kleinberg, and Tardos in KDD 2003). While the progressive models are suitable for modeling the spread of influence in monopolistic settings, non-progressive are more appropriate for modeling non-monopolistic settings, e.g., modeling diffusion of two competing technologies over a social network. Despite the extensive work on the progressive model, non-progressive models have not been studied well. In this paper, we study the spread of influence in the non-progressive model under the strict majority threshold: given a graph $G$ with a set of initially infected nodes, each node gets infected at time $\tau$ iff a majority of its neighbors are infected at time $\tau-1$. Our goal in the \textit{MinPTS} problem is to find a minimum-cardinality initial set of infected nodes that would eventually converge to a steady state where all nodes of $G$ are infected. #R##N#We prove that while the MinPTS is NP-hard for a restricted family of graphs, it admits an improved constant-factor approximation algorithm for power-law graphs. We do so by proving lower and upper bounds in terms of the minimum and maximum degree of nodes in the graph. The upper bound is achieved in turn by applying a natural greedy algorithm. Our experimental evaluation of the greedy algorithm also shows its superior performance compared to other algorithms for a set of real-world graphs as well as the random power-law graphs. Finally, we study the convergence properties of these algorithms and show that the non-progressive model converges in at most $O(|E(G)|)$ steps.

Connected through hyperedge HE-1 (induced by source paper "whom to befriend to influence people"):
  with members:
    - "selecting nodes and buying links to maximize the information diffusion in a network"
    - "recommending links through influence maximization"
    - "discovering small target sets in social networks a fast and effective algorithm"
    - "spread of influence in weighted networks under time and budget constraints"
    - "contagious sets in dense graphs"
    - "parameterized approximability of maximizing the spread of influence in networks"
    - "latency bounded target set selection in social networks"
    - "how to influence people with partial incentives"

Connected through hyperedge HE-2 (induced by source paper "modeling non progressive phenomena for influence propagation"):
  with members:
    - "scalable influence estimation in continuous time diffusion networks"
    - "influence maximization in continuous time diffusion networks"
    - "a data based approach to social influence maximization"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"

Connected through hyperedge HE-3 (induced by source paper "complexity of equilibrium in diffusion games on social networks"):
  with members:
    - "opinion maximization in social networks"
    - "opinion dynamics in social networks a local interaction game with stubborn agents"
    - "competitive contagion in networks"
    - "nash equilibria in voronoi games on graphs"

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)