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: "improving adaptation of ubiquitous recommander systems by using reinforcement learning and collaborative filtering"
  Abstract: The wide development of mobile applications provides a considerable amount of data of all types (images, texts, sounds, videos, etc.). Thus, two main issues have to be considered: assist users in finding information and reduce search and navigation time. In this sense, context-based recommender systems (CBRS) propose the user the adequate information depending on her/his situation. Our work consists in applying machine learning techniques and reasoning process in order to bring a solution to some of the problems concerning the acceptance of recommender systems by users, namely avoiding the intervention of experts, reducing cold start problem, speeding learning process and adapting to the user's interest. To achieve this goal, we propose a fundamental modification in terms of how we model the learning of the CBRS. Inspired by models of human reasoning developed in robotic, we combine reinforcement learning and case-based reasoning to define a contextual recommendation process based on different context dimensions (cognitive, social, temporal, geographic). This paper describes an ongoing work on the implementation of a CBRS based on a hybrid Q-learning (HyQL) algorithm which combines Q-learning, collaborative filtering and case-based reasoning techniques. It also presents preliminary results by comparing HyQL and the standard Q-Learning w.r.t. solving the cold start problem.

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)