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: "detection and isolation of failures in directed networks of lti systems"
  Abstract: We propose a methodology to detect and isolate link failures in a weighted and directed network of identical multi-input multioutput linear time-invariant (LTI) systems when only the output responses of a subset of nodes are available. Our method is based on the observation of jump discontinuities in the output derivatives, which can be explicitly related to the occurrence of link failures. The order of the derivative at which the jump is observed is given by    $r(d+1)$  , where    $r$   is the relative degree of each system's transfer matrix, and    $d$   denotes the distance from the location of the failure to the observation point. We then propose detection and isolation strategies based on this relation. Furthermore, we propose an efficient algorithm for sensor placement to detect and isolate any possible link failure using a small number of sensors. Available results from the theory of submodular set functions provide us with performance guarantees that bound the size of the chosen sensor set within a logarithmic factor of the smallest feasible set of sensors. These results are illustrated through elaborative examples and supplemented by computer experiments.

Connected through hyperedge HE-1 (induced by source paper "scheduling resource bounded monitoring devices for event detection and isolation in networks"):
  with members:
    - "sensor placement for fault location identification in water networks a minimum test cover approach"
    - "set k cover algorithms for energy efficient monitoring in wireless sensor networks"

Connected through hyperedge HE-2 (induced by source paper "detecting topology variations in dynamical networks"):
  with members:
    - "detection and isolation of failures in linear multi agent networks"

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)