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: "exploring invariants patterns in human commute time"
  Abstract: In everyday life, the process of commuting to work from home happens every now and then. And the research of commute characteristics is useful for urban function planning. For humans, the commute of an individual seems revealing no regular universal patterns, but it is true that people try to find a satisfactory state of life regarding commute issues. Commute time and distance are most important indicators to measure the degree of this satisfaction. Marchetti states a certain regularity in human commute time distribution - specifically, it states that no matter when, where and how far away people live, they always tend to spend approximately the same average time for their daily commute. However, will the rapid development of cities nowadays as well as serious challenges brought by economic development affect this constant? If there are novel characteristics? We revisit these problems using fine grained communication data in two Chinese major cities during recent two years. The results indicate that the commute time has been slightly increased from Marchetti's constant with the development of society. People's overall travel budgets have been increased, more concretely speaking, for medium and long distance commuters, their endurance limit for commute time is enhanced during the passing years, and fluctuates around a constant; for short distance commuters, their commute time increases with the distance. Moreover, the population distribution in every commute distance shows strong cross-city similarity and does not change much over two years.

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.CY(Computers and Society)