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: "minimizing convergence error in multi agent systems via leader selection a supermodular optimization approach"
  Abstract: In a leader-follower multi-agent system (MAS), the leader agents act as control inputs and influence the states of the remaining follower agents. The rate at which the follower agents converge to their desired states, as well as the errors in the follower agent states prior to convergence, are determined by the choice of leader agents. In this paper, we study leader selection in order to minimize convergence errors experienced by the follower agents, which we define as a norm of the distance between the follower agents' intermediate states and the convex hull of the leader agent states. By introducing a novel connection to random walks on the network graph, we show that the convergence error has an inherent supermodular structure as a function of the leader set. Supermodularity enables development of efficient discrete optimization algorithms that directly approximate the optimal leader set, provide provable performance guarantees, and do not rely on continuous relaxations. We formulate two leader selection problems within the supermodular optimization framework, namely, the problem of selecting a fixed number of leader agents in order to minimize the convergence error, as well as the problem of selecting the minimum-size set of leader agents to achieve a given bound on the convergence error. We introduce algorithms for approximating the optimal solution to both problems in static networks, dynamic networks with known topology distributions, and dynamic networks with unknown and unpredictable topology distributions. Our approach is shown to provide significantly lower convergence errors than existing random and degree-based leader selection methods in a numerical study.

Connected through hyperedge HE-1 (induced by source paper "shifting opinions in a social network through leader selection"):
  with members:
    - "current flow group closeness centrality for complex networks"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "opinion dynamics with stubborn agents"
    - "minimizing polarization and disagreement in social networks"
    - "from local averaging to emergent global behaviors the fundamental role of network interconnections"
    - "opinion maximization in social networks"
    - "an efficient parallel solver for sdd linear systems"
    - "submodular optimization with submodular cover and submodular knapsack constraints"

Connected through hyperedge HE-2 (induced by source paper "minimal reachability is hard to approximate"):
  with members:
    - "minimal inputs outputs for a networked system"
    - "minimal reachability problems"
    - "variable selection is hard"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "on the structure and computation of random walk times in finite graphs"):
  with members:
    - "optimal temporal logic planning in probabilistic semantic maps"
    - "large scale spectral clustering using resistance distance and spielman teng solvers"
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"
    - "opinion dynamics in social networks a local interaction game with stubborn agents"
    - "mdp optimal control under temporal logic constraints"

Connected through hyperedge HE-4 (induced by source paper "maximizing the smallest eigenvalue of a symmetric matrix a submodular optimization approach"):
  with members:
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"
    - "opinion dynamics in social networks a local interaction game with stubborn agents"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"

Connected through hyperedge HE-5 (induced by source paper "submodularity in input node selection for networked systems"):
  with members:
    - "minimal actuator placement with optimal control constraints"
    - "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-6 (induced by source paper "minimal actuator placement with optimal control constraints"):
  with members:
    - "controllability metrics limitations and algorithms for complex networks"
    - "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)