human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning from incomplete ratings using nonlinear multi layer semi nonnegative matrix factorization"):
  Abstract: Recommender systems problems witness a growing interest for finding better learning algorithms for personalized information. Matrix factorization that estimates the user liking for an item by taking an inner product on the latent features of users and item have been widely studied owing to its better accuracy and scalability. However, it is possible that the mapping between the latent features learned from these and the original features contains rather complex nonlinear hierarchical information, that classical linear matrix factorization can not capture. In this paper, we aim to propose a novel multilayer non-linear approach to a variant of nonnegative matrix factorization (NMF) to learn such factors from the incomplete ratings matrix. Firstly, we construct a user-item matrix with explicit ratings, secondly we learn latent factors for representations of users and items from the designed nonlinear multi-layer approach. Further, the architecture is built with different nonlinearities using adaptive gradient optimizer to better learn the latent factors in this space. We show that by doing so, our model is able to learn low-dimensional representations that are better suited for recommender systems on several benchmark datasets.

Includes members:
  Member "deep learning based recommender system a survey and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "collaborative deep learning for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

Question: Please tell me which class the source paper 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.LG(Machine Learning)