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: "personalizing graph neural networks with attention mechanism for session based recommendation"
  Abstract: The problem of personalized session-based recommendation aims to predict users' next click based on their sequential behaviors. Existing session-based recommendation methods only consider all sessions of user as a single sequence, ignoring the relationship of among sessions. Other than that, most of them neglect complex transitions of items and the collaborative relationship between users and items. To this end, we propose a novel method, named Personalizing Graph Neural Networks with Attention Mechanism, A-PGNN for brevity. A-PGNN mainly consists of two components: One is Personalizing Graph Neural Network (PGNN), which is used to capture complex transitions in user session sequence. Compared with the traditional Graph Neural Network (GNN) model, it also considers the role of users in the sequence. The other is Dot-Product Attention mechanism, which draws on the attention mechanism in machine translation to explicitly model the effect of historical sessions on the current session. These two parts make it possible to learn the multi-level transition relationships between items and sessions in user-specific fashion. Extensive experiments conducted on two real-world data sets show that A-PGNN significantly outperforms the state-of-the-art personalizing session-based recommendation methods consistently.

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)