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: "asymptotic network robustness"
  Abstract: This paper examines the dependence of performance measures on network size with a focus on large networks. We develop a framework where it is shown that poor performance can be attributed to dimension--dependent scaling of network energy. Drawing from previous work, we show that such scaling in undirected networks can be attributed to the proximity of network spectrum to unity, or distance to instability. However, such a simple characterization does not exist for the case of directed networks. In fact, we show that any arbitrary scaling can be achieved for a fixed distance to instability. The results here demonstrate that it is always favorable, in terms of performance scaling, to balance a large network. This justifies a popular observation that undirected networks generally perform better. We show the relationship between network energy and performance measures, such as output shortfall probability or centrality, that are used in economic or financial networks. The strong connection between them explains why a network topology behaves qualitatively similar under different performance measures. Our results suggest that there is a need to study performance degradation in large networks that focus on topological dependence and transcend application specific analyses.

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)