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 "eigenrec generalizing puresvd for effective and efficient top n recommendations"):
  Abstract: We introduce EigenRec, a versatile and efficient latent factor framework for top-N recommendations that includes the well-known PureSVD algorithm as a special case. EigenRec builds a low-dimensional model of an inter-item proximity matrix that combines a similarity component, with a scaling operator, designed to control the influence of the prior item popularity on the final model. Seeing PureSVD within our framework provides intuition about its inner workings, exposes its inherent limitations, and also, paves the path toward painlessly improving its recommendation performance. A comprehensive set of experiments on the MovieLens and the Yahoo datasets based on widely applied performance metrics, indicate that EigenRec outperforms several state-of-the-art algorithms, in terms of Standard and Long-Tail recommendation accuracy, exhibiting low susceptibility to sparsity, even in its most extreme manifestations—the Cold-Start problems. At the same time, EigenRec has an attractive computational profile and it can apply readily in large-scale recommendation settings.

Includes members:
  Member "unbiased learning to rank with biased feedback":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "safe exploration for optimizing contextual bandits")
  Member "primme_svds a high performance preconditioned svd solver for accurate large scale computations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable global alignment graph kernel using random features from node embedding to graph embedding")
  Member "recommendations as treatments debiasing learning and evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the fairness of randomized trials for recommendation with heterogeneous demographics and beyond")
  Member "feature based factorized bilinear similarity model for cold start top n item recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a collective variational autoencoder for top n recommendation with side information")
  Member "a preconditioned hybrid svd method for accurately computing singular triplets of large matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "word mover s embedding from word2vec to document embedding")
  Member "challenging the long tail recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scaling up collaborative filtering data sets through randomized fractal expansions")

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.IR(Information Retrieval)