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 "multi robot task allocation with resource contention and uncertain timing"):
  Abstract: This paper proposes an analytical framework for modelling resource contention in multi-robot systems, where the travel times and task durations are uncertain. It uses several approximation methods to quickly and accurately calculate the probability distributions describing the times that tasks start and finish. Specific contributions include a method for calculating the probability of a set of independent normally distributed random events occurring in a given order, an upper bound on that probability, and a method for calculating the most likely and $n$-th most likely orders of occurrence for a set of independent normally distributed random events that have equal standard deviations. The complete framework is shown to be much faster than a Monte Carlo approach for the same accuracy in two multi-robot task allocation problems. This is a general framework that is agnostic to the optimisation method and objective function used, and is applicable to a wide range of robotics and non-robotics problems.

Includes members:
  Member "methods for stochastic collection and replenishment scar optimisation for persistent autonomy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "modelling resource contention in multi robot task allocation problems with uncertain timing")
  Member "applying gaussian distributed constraints to gaussian distributed variables":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "methods for stochastic collection and replenishment scar optimisation for persistent autonomy")
  Member "stochastic collection and replenishment scar optimisation for persistent autonomy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "methods for stochastic collection and replenishment scar optimisation for persistent autonomy")
  Member "stochastic collection and replenishment scar objective functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "methods for stochastic collection and replenishment scar optimisation for persistent autonomy")
  Member "trust based mechanisms for robust and efficient task allocation in the presence of execution uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "profitable task allocation in mobile cloud computing")

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.MA(Multiagent Systems)