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: "formation of stable strategic networks with desired topologies"
  Abstract: Abstract Many real-world networks, such as social networks, consist of strategic agents. The topology of these networks often plays a crucial role in determining the ease and speed with which certain information-driven tasks can be accomplished. Consequently, growing a stable network of a certain desired topology is of interest. Motivated by this, we study the following important problem: Given a certain desired topology, under what conditions would best response link alteration strategies adopted by strategic agents lead to formation of a stable network having the given topology and no other topology. This problem is the inverse of the classical network formation problem where we are concerned with determining stable topologies, given the conditions on the network parameters. We study this interesting inverse problem by proposing (1) a recursive model of network formation and (2) a utility model that captures key determinants of network formation. Building upon these models, we explore relevant topologies such as star graph complete graph, bipartite TurAin graph, and multiple stars with interconnected centres. We derive a set of sufficient conditions under which these topologies uniquely emerge, study their social welfare properties and investigate the effects of deviating from the derived conditions.

Connected through hyperedge HE-1 (induced by source paper "new models and methods for formation and analysis of social networks"):
  with members:
    - "preference elicitation for general random utility models"
    - "the role of social networks in information diffusion"
    - "incentive compatible influence maximization in social networks and application to viral marketing"
    - "competitive contagion in networks"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

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)