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: "caps context aware personalized poi sequence recommender system"
  Abstract: The revolution of World Wide Web (WWW) and smart-phone technologies have been the key-factor behind remarkable success of social networks. With the ease of availability of check-in data, the location-based social networks (LBSN) (e.g., Facebook1, etc.) have been heavily explored in the past decade for Point-of-Interest (POI) recommendation. Though many POI recommenders have been defined, most of them have focused on recommending a single location or an arbitrary list that is not contextually coherent. It has been cumbersome to rely on such systems when one needs a contextually coherent list of locations, that can be used for various day-to-day activities, for e.g., itinerary planning. This paper proposes a model termed as CAPS (Context-Aware Personalized POI Sequence Recommender System) that generates contextually coherent POI sequences relevant to user preferences. To the best of our knowledge, CAPS is the first attempt to formulate the contextual POI sequence modeling by extending Recurrent Neural Network (RNN) and its variants. CAPS extends RNN by incorporating multiple contexts to the hidden layer and by incorporating global context (sequence features) to the hidden layers and the output layer. It extends the variants of RNN (e.g., Long-short term memory (LSTM)) by incorporating multiple contexts and global features in the gate update relations. The major contributions of this paper are: (i) it models the contextual POI sequence problem by incorporating personalized user preferences through multiple constraints (e.g., categorical, social, temporal, etc.), (ii) it extends RNN to incorporate the contexts of individual item and that of the whole sequence. It also extends the gated functionality of variants of RNN to incorporate the multiple contexts, and (iii) it evaluates the proposed models against two real-world data sets.

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)