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: "applying gaussian distributed constraints to gaussian distributed variables"
  Abstract: Develops an analytical method of truncating constrained Gaussian distributed variables.The constraints are described by Gaussian distributions.Method is applied to the constrained Kalman filtering problem.Outperforms existing methods in a simulation example. This paper develops an analytical method of truncating inequality constrained Gaussian distributed variables where the constraints are themselves described by Gaussian distributions. Existing truncation methods either assume hard constraints, or use numerical methods to handle uncertain constraints. The proposed approach introduces moment-based Gaussian approximations of the truncated distribution. This method can be applied to numerous problems, with the motivating problem being Kalman filtering with uncertain constraints. In a simulation example, the developed method is shown to outperform unconstrained Kalman filtering by over 40% and hard-constrained Kalman filtering by over 17%.

Connected through hyperedge HE-1 (induced by source paper "methods for stochastic collection and replenishment scar optimisation for persistent autonomy"):
  with members:
    - "stochastic collection and replenishment scar optimisation for persistent autonomy"
    - "stochastic collection and replenishment scar objective functions"

Connected through hyperedge HE-2 (induced by source paper "multi robot task allocation with resource contention and uncertain timing"):
  with members:
    - "methods for stochastic collection and replenishment scar optimisation for persistent autonomy"
    - "stochastic collection and replenishment scar optimisation for persistent autonomy"
    - "stochastic collection and replenishment scar objective functions"
    - "trust based mechanisms for robust and efficient task allocation in the presence of execution uncertainty"

Connected through hyperedge HE-3 (induced by source paper "modelling resource contention in multi robot task allocation problems with uncertain timing"):
  with members:
    - "methods for stochastic collection and replenishment scar optimisation for persistent autonomy"

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.SY(Systems and Control)