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: "asynchronous rumour spreading in social and signed topologies"
  Abstract: In this paper, we present an experimental analysis of the asynchronous push & pull rumour spreading protocol. This protocol is, to date, the best-performing rumour spreading protocol for simple, scalable, and robust information dissemination in distributed systems. We analyse the effect that multiple parameters have on the protocol's performance, such as using memory to avoid contacting the same neighbor twice in a row, varying the stopping criteria used by nodes to decide when to stop spreading the rumour, employing more sophisticated neighbor selection policies instead of the standard uniform random choice, and others. Prior work has focused on either providing theoretical upper bounds regarding the number of rounds needed to spread the rumour to all nodes, or, proposes improvements by adjusting isolated parameters. To our knowledge, our work is the first to study how multiple parameters affect system behaviour both in isolation and combination and under a wide range of values. Our analysis is based on experimental simulations using real-world social network datasets, thus complementing prior theoretical work to shed light on how the protocol behaves in practical, real-world systems. We also study the behaviour of the protocol on a special type of social graph, called signed networks (e.g., Slashdot and Epinions), whose links indicate stronger trust relationships. Finally, through our detailed analysis, we demonstrate how a few simple additions to the protocol can improve the total time required to inform 100% of the nodes by a maximum of 99.69% and an average of 82.37%.

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)