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: "minimal actuator placement with optimal control constraints"
  Abstract: We introduce the problem of minimal actuator placement in a linear control system so that a bound on the minimum control effort for a given state transfer is satisfied while controllability is ensured. We first show that this is an NP-hard problem following the recent work of Olshevsky. Next, we prove that this problem has a supermodular structure. Afterwards, we provide an efficient algorithm that approximates up to a multiplicative factor of O(logn), where n is the size of the multi-agent network, any optimal actuator set that meets the specified energy criterion. Moreover, we show that this is the best approximation factor one can achieve in polynomial-time for the worst case. Finally, we test this algorithm over large Erdos-Renyi random networks to further demonstrate its efficiency.

Connected through hyperedge HE-1 (induced by source paper "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"):
  with members:
    - "scheduling nonlinear sensors for stochastic process estimation"
    - "lazier than lazy greedy"

Connected through hyperedge HE-2 (induced by source paper "sensor selection cost optimisation for tracking structurally cyclic systems a p order solution"):
  with members:
    - "graph theoretic distributed inference in social networks"
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"

Connected through hyperedge HE-3 (induced by source paper "submodularity in input node selection for networked systems"):
  with members:
    - "minimizing convergence error in multi agent systems via leader selection a supermodular optimization approach"
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"
    - "near optimal nonmyopic value of information in graphical models"

Connected through hyperedge HE-4 (induced by source paper "minimal reachability problems"):
  with members:
    - "controllability metrics limitations and algorithms for complex networks"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"

Connected through hyperedge HE-5 (induced by source paper "gramian based reachability metrics for bilinear networks"):
  with members:
    - "controllability metrics limitations and algorithms for complex 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)