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: "learning to effectively select topics for information retrieval test collections"
  Abstract: While test collections are commonly employed to evaluate the effectiveness of information retrieval (IR) systems, constructing these collections has become increasingly expensive in recent years as collection sizes have grown ever-larger. To address this, we propose a new learning-to-rank topic selection method which reduces the number of search topics needed for reliable evaluation of IR systems. As part of this work, we also revisit the deep vs. shallow judging debate: whether it is better to collect many relevance judgments for a few topics or a few judgments for many topics. We consider a number of factors impacting this trade-off: how topics are selected, topic familiarity to judges, and how topic generation cost may impact both budget utilization and the resultant quality of judgments. Experiments on NIST TREC Robust 2003 and Robust 2004 test collections show not only our method's ability to reliably evaluate IR systems using fewer topics, but also that when topics are intelligently selected, deep judging is often more cost-effective than shallow judging in achieving the same level of evaluation reliability. Topic familiarity and construction costs are also seen to notably impact the evaluation cost vs. reliability tradeoff and provide further evidence supporting deep judging in practice.

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.IR(Information Retrieval)