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: "distance landmarks revisited for road graphs"
  Abstract: Computing shortest distances is one of the funda- mental problems on graphs, and remains a challenging task today. Distance landmarks have been recently studied for shortest distance queries with an auxiliary data structure, referred to as landmark covers. This paper studies how to apply distance landmarks for fast exact shortest distance query answering on large road graphs. However, the direct application of distance landmarks is impractical due to the high space and time cost. To rectify this problem, we investigate novel techniques that can be seamlessly combined with distance landmarks. We first propo se a notion of hybrid landmark covers, a revision of landmark covers. Second, we propose a notion of agents, each of which represents a small subgraph and holds good properties for fast distance query answering. We also show that agents can be computed in linear time. Third, we introduce graph partitions to deal with the remaining subgraph that cannot be captured by agents. Fourth, we develop a unified framework that seamlessly integrates ou r proposed techniques and existing optimization techniques, for fast shortest distance query answering. Finally, we experimentally verify that our techniques significantly improve the efficie ncy of shortest distance queries, using real-life road graphs. I. I NTRODUCTION

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.DB(Databases)