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 "coping with unreliable workers in internet based computing an evaluation of reputation mechanisms"):
  Abstract: We present reputation-based mechanisms for building reliable task computing systems over the Internet. The most characteristic examples of such systems are the volunteer computing and the crowdsourcing platforms. In both examples end users are offering over the Internet their computing power or their human intelligence to solve tasks either voluntarily or under payment. While the main advantage of these systems is the inexpensive computational power provided, the main drawback is the untrustworthy nature of the end users. Generally, this type of systems are modeled under the "master-worker" setting. A "master" has a set of tasks to compute and instead of computing them locally she sends these tasks to available "workers" that compute and report back the task results. We categorize these workers in three generic types: altruistic, malicious and rational. Altruistic workers that always return the correct result, malicious workers that always return an incorrect result, and rational workers that decide to reply or not truthfully depending on what increases their benefit. We design a reinforcement learning mechanism to induce a correct behavior to rational workers, while the mechanism is complemented by four reputation schemes that cope with malice. The goal of the mechanism is to reach a state of eventual correctness, that is, a stable state of the system in which the master always obtains the correct task results. Analysis of the system gives provable guarantees under which truthful behavior can be ensured. Finally, we observe the behavior of the mechanism through simulations that use realistic system parameters values. Simulations not only agree with the analysis but also reveal interesting trade-offs between various metrics and parameters. Finally, the four reputation schemes are assessed against the tolerance to cheaters.

Includes members:
  Member "asynchrony and collusion in the n party bar transfer problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a resource competitive jamming defense")
  Member "reputation based incentive protocols in crowdsourcing applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prospect theory based crowdsourcing for classification in the presence of spammers")

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.DC(Distributed, Parallel, and Cluster Computing)