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 reachability problems"
  Abstract: In this paper, we address a collection of state space reachability problems, for linear time-invariant systems, using a minimal number of actuators. In particular, we design a zero-one diagonal input matrix B, with a minimal number of non-zero entries, so that a specified state vector is reachable from a given initial state. Moreover, we design a B so that a system can be steered either into a given subspace, or sufficiently close to a desired state. This work extends the recent results of Olshevsky and Pequito, where a zero-one diagonal or column matrix B is constructed so that the involved system is controllable. Specifically, we prove that the first two of our aforementioned problems are NP-hard; these results hold for a zero-one column matrix B as well. Then, we provide efficient polynomial time algorithms for their general solution, along with their worst case approximation guarantees. Finally, we illustrate their performance over large random networks.

Connected through hyperedge HE-1 (induced by source paper "deterministic and randomized actuator scheduling with guaranteed performance bounds"):
  with members:
    - "an alon boppana type bound for weighted graphs and lowerbounds for spectral sparsification"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "minimal reachability is hard to approximate"
    - "structural analysis and optimal design of distributed system throttlers"
    - "control principles of complex networks"
    - "graph distances and controllability of networks"
    - "controllability metrics limitations and algorithms for complex networks"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-2 (induced by source paper "herding positive complex networks"):
  with members:
    - "control principles of complex networks"
    - "controllability metrics limitations and algorithms for complex networks"

Connected through hyperedge HE-3 (induced by source paper "minimal reachability is hard to approximate"):
  with members:
    - "minimal inputs outputs for a networked system"
    - "variable selection is hard"
    - "minimizing convergence error in multi agent systems via leader selection a supermodular optimization approach"
    - "controllability metrics limitations and algorithms for complex networks"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"

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)