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: "building robust crowdsourcing systems with reputation aware decision support techniques"
  Abstract: Crowdsourcing refers to the arrangement in which contributions are solicited from a large group of unrelated people. Due to this nature, crowdsourcers (or task requesters) often face uncertainty about the workers' capabilities which, in turn, affects the quality and timeliness of the results obtained. Trust is a mechanism used by people to facilitate interactions in human societies where risk and uncertain are common. The crucial challenge to building a robust crowdsourcing system is how to make trust-aware task delegation decisions to efficiently utilize the capacities of workers (or trustee agents) to achieve high social welfare? #R##N#This book presents the research addressing this challenge. It goes beyond the existing trust management research framework by removing a widespread assumption implicitly adopted by existing research: that a trustee agent can process an unlimited number of interaction requests per discrete time unit without compromising its performance as perceived by the task requesters (or truster agents). Decision support in crowdsourcing is re-formalized as a multi-agent trust game based on the principles of the Congestion Game, which is solved by two trust-aware interaction decision-making approaches: 1) the Social Welfare Optimizing approach for Reputation-aware Decision-making (SWORD) approach, and 2) the Distributed Request Acceptance approach for Fair utilization of Trustee agents (DRAFT). SWORD is designed for centralized systems, while DRAFT is designed for fully distributed systems. Theoretical analyses have shown that the social welfare produced by these two approaches can be made closer to optimal by adjusting only one key parameter. With these two approaches, the framework of research for crowdsourcing systems can be enriched to handle more realistic scenarios where workers have varied and limited capabilities.

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.MA(Multiagent Systems)