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: "nonexpansive piecewise constant hybrid systems are conservative"
  Abstract: Consider a partition of $R^n$ into finitely many polyhedral regions $D_i$ and associated drift vectors $\mu_i\in R^n$. We study ``hybrid'' dynamical systems whose trajectories have a constant drift, $\dot x=\mu_i$, whenever $x$ is in the interior of the $i$th region $D_i$, and behave consistently on the boundary between different regions. Our main result asserts that if such a system is nonexpansive (i.e., if the Euclidean distance between any pair of trajectories is a nonincreasing function of time), then the system must be conservative, i.e., its trajectories are the same as the trajectories of the negative subgradient flow associated with a potential function. Furthermore, this potential function is necessarily convex, and is linear on each of the regions $D_i$. We actually establish a more general version of this result, by making seemingly weaker assumptions on the dynamical system of interest.

Connected through hyperedge HE-1 (induced by source paper "when do trajectories have bounded sensitivity to cumulative perturbations"):
  with members:
    - "fluctuation bounds for the max weight policy with applications to state space collapse"
    - "sensitivity to cumulative perturbations for a class of piecewise constant hybrid systems"
    - "cone schedules for processing systems in fluctuating environments"

Connected through hyperedge HE-2 (induced by source paper "sensitivity to cumulative perturbations for a class of piecewise constant hybrid systems"):
  with members:
    - "when do trajectories have bounded sensitivity to cumulative perturbations"
    - "fluctuation bounds for the max weight policy with applications to state space collapse"
    - "cone schedules for processing systems in fluctuating environments"

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)