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: "the k shortest paths problem with application to routing"
  Abstract: While much work has proposed novel solutions to finding the $k$ shortest simple paths between two nodes, very little research has addressed the challenges in designing efficient solutions for real world networks. More specifically, the computational complexity for solutions to the $k$ shortest simple paths problem on positively weighted graphs typically include an $O(km)$ term, where $m$ is the number of edges in the graph. And even though an almost shortest simple path in an arbitrary graph could consist of $O(m)$ edges, in real world networks, such a solution appears asymptotically inefficient as such paths often scale logarithmically with the size of the network. #R##N#To address this gap, we provide both theoretical results and simulations illustrating that the ratio between simple paths and weakly simple paths of the same length is well behaved for a range of parameters in the Chung-Lu random graph model. Since the Chung-Lu random graph model captures many properties of real world networks, these results strongly suggest that in application, solutions to the $k$ shortest simple path problem should first identify a slightly larger collection of almost shortest paths and then remove the paths that contain loops. Furthermore, we present a simple algorithm for computing all (weakly simple) paths between two nodes bounded by length $D$ in $O(m\log m+kL)$ time for graphs with positive edge weights, where $L$ is an upperbound for the number of nodes in any returned path and $k$ is the number paths computed. We then consider an application to the almost shortest paths algorithm to measure path diversity for internet routing in a snapshot of the Autonomous System graph subject to an edge deletion process.

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)