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: "deep item based collaborative filtering for sparse implicit feedback"
  Abstract: Recommender systems are ubiquitous in the domain of e-commerce, used to improve the user experience and to market inventory, thereby increasing revenue for the site. Techniques such as item-based collaborative filtering are used to model users' behavioral interactions with items and make recommendations from items that have similar behavioral patterns. However, there are challenges when applying these techniques on extremely sparse and volatile datasets. On some e-commerce sites, such as eBay, the volatile inventory and minimal structured information about items make it very difficult to aggregate user interactions with an item. In this work, we describe a novel deep learning-based method to address the challenges. We propose an objective function that optimizes a similarity measure between binary implicit feedback vectors between two items. We demonstrate formally and empirically that a model trained to optimize this function estimates the log of the cosine similarity between the feedback vectors. We also propose a neural network architecture optimized on this objective. We present the results of experiments comparing the output of the neural network with traditional item-based collaborative filtering models on an implicit-feedback dataset, as well as results of experiments comparing different neural network architectures on user purchase behavior on eBay. Finally, we discuss the results of an A/B test that show marked improvement of the proposed technique over eBay's existing collaborative filtering recommender 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)