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: "a computational approach to organizational structure"
  Abstract: An organizational structure defines how an organization arranges and manages its individuals. Because individuals in an organization take time to both perform tasks and communicate, organizational structure must take both computation and communication into account. Sociologists have studied organizational structure from an empirical perspective for decades, but their work has been limited by small sample sizes. By contrast, we study organizational structure from a computational and theoretical perspective. #R##N#Specifically, we introduce a model of organizations that involves both computation and communication, and captures the spirit of past sociology experiments. In our model, each node in a graph starts with a token, and at any time can either perform computation to merge two tokens in $t_c$ time, or perform communication by sending a token to a neighbor in $t_m$ time. We study how to schedule computations and communications so as to merge all tokens as quickly as possible. #R##N#As our first result, we give a polynomial-time algorithm that optimally solves this problem on a complete graph. This result characterizes the optimal graph structure---the edges used for communication in the optimal schedule---and therefore informs the optimal design of large-scale organizations. Moreover, since pre-existing organizations may want to optimize their workflow, we also study this problem on arbitrary graphs. We demonstrate that our problem on arbitrary graphs is not only NP-hard but also hard to approximate within a multiplicative $1.5$ factor. Finally, we give an $O(\log n \cdot \log \frac{\text{OPT}}{t_m})$-approximation algorithm for our problem on arbitrary graphs.

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.DS(Data Structures and Algorithms)