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: "adquex adaptive processing of federated queries over linked data based on tuple routing"
  Abstract: Recent achievements of linked data implementations and the increased number of datasets available on the web as linked data, has given rise to the need and tendency toward processing federated queries over these datasets. Due to the d istribution of linked data across the web, the methods that process federated queries through a di stributed approach are more attractive to the users and have gained more prosperity. In distribut ed processing of federated queries, we need methods and procedures to execute the query in an o ptimal manner. Most of the existing methods perform the optimization task based on some statist ical information, whereas the query processor does not have precise statistical information about their properties, since the data sources are autonomous. When precise statistics are not availab le, the possibility of wrong estimations will highl y increase, and may lead to inefficient execution of the query at runtime. Another problem of the existing methods is that in the optimization phase, they assume that runtime conditions of query execution are stable, while the environment in whic h federated queries are executed over linked data is dynamic and non-predictable. By considering thes e two problems, there is a great potential for exploiting the federated query processing technique s in an adaptive manner. In this paper, an adaptive method is proposed for processing federate d queries over linked data, based on the concept of routing the tuples. The proposed method, named A DQUEX, is able to execute the query effectively without any prior statistical information. This met hod can change the query execution plan at runtime so that less intermediate results are produced. It can also adapt the execution plan to new situation if unpredicted network latencies arise. Extensive eval uation of our method by running real queries over well-known linked datasets shows very good results especially for complex queries.

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.DC(Distributed, Parallel, and Cluster Computing)