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: "interpretable recommendations via overlapping co clusters"
  Abstract: There is an increasing need to provide not only accurate but also interpretable recommendations, in order to enhance transparency and trust in the recommendation process. This is particularly important in a business-to-business setting, where recommendations are generated for experienced sales staff and not directly for the end-client. In this paper, we consider the problem of generating interpretable recommendations based on the purchase history of clients, or more general, based on positive or one-class ratings only. We present an algorithm that generates recommendations by identifying overlapping co-clusters consisting of clients and products. Our algorithm uses matrix factorization techniques to identify co-clusters, and recommends a client-product pair because of its membership in one or more client-product co-clusters. The algorithm exhibits linear complexity in the number of co-clusters and input examples, and can therefore be applied to very large datasets. We show, both on a real client-product dataset from our institution, as well as on publicly available datasets, that the recommendation accuracy of our algorithm is better than or equivalent to standard interpretable and non-interpretable recommendation techniques, such as standard one-class nearest neighbor and matrix factorization techniques. Most importantly, our approach is capable of offering textually and visually interpretable recommendations.

Connected through hyperedge HE-1 (induced by source paper "visually explainable recommendation"):
  with members:
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "neural collaborative filtering"
    - "neural rating regression with abstractive tips generation for recommendation"
    - "explainable recommendation theory and applications"
    - "a visual and textual recurrent neural network for sequential prediction"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "explaining reviews and ratings with paco poisson additive co clustering"
    - "vbpr visual bayesian personalized ranking from implicit feedback"

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)