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: "limits to surprise in recommender systems"
  Abstract: In this study, we address the challenge of measuring the ability of a recommender system to make surprising recommendations. Although current evaluation methods make it possible to determine if two algorithms can make recommendations with a significant difference in their average surprise measure, it could be of interest to our community to know how competent an algorithm is at embedding surprise in its recommendations, without having to resort to making a direct comparison with another algorithm. We argue that a) surprise is a finite resource in a recommender system, b) there is a limit to how much surprise any algorithm can embed in a recommendation, and c) this limit can provide us with a scale against which the performance of any algorithm can be measured. By exploring these ideas, it is possible to define the concepts of maximum and minimum potential surprise and design a surprise metric called "normalised surprise" that employs these limits to potential surprise. Two experiments were conducted to test the proposed metric. The aim of the first was to validate the quality of the estimates of minimum and maximum potential surprise produced by a greedy algorithm. The purpose of the second experiment was to analyse the behaviour of the proposed metric using the MovieLens dataset. The results confirmed the behaviour that was expected, and showed that the proposed surprise metric is both effective and consistent for differing choices of recommendation algorithms, data representations and distance functions.

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)