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: "resygen a recommendation system generator using domain based heuristics"
  Abstract: Recommender systems provide personalized recommendations on products or services to user. The amount information handled by this type of systems is steadily growing. Furthermore, the development of recommendation systems is a difficult task due to the implementation of complex algorithms and metrics. For this reason, the success of recommendation systems depends on preliminary design decisions such as the most adequate similarity metric, the right process to infer proactive recommendations, for mentioning a few. This decision determines the process for generating recommendations and also impacts quality and user's satisfaction. In this paper, we propose RESYGEN, a Recommendation System Generator. RESYGEN allows the user to generate such kind of systems in an easy and friendly way. Furthermore, RESYGEN allows the generation of multi-domain systems such as music, video, books, travel, hardware, software, and food to mention a few. RESYGEN is based in the selection of the best distance metrics for nominal, ordinal, numeric and binary attributes, with the aim to reduce complexity for non-expert users and also to facilitate the selection of the metric which best fits to the data type. A system generated through RESYGEN has several interesting elements such as ratings, recommendations, cloud tag, among others. We performed a qualitative evaluation with the aim of comparing other recommender systems against systems generated by RESYGEN. The results shows that generated systems by RESYGEN, comprise the basic elements of a recommendation system.

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)