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: "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.

Connected through hyperedge HE-1 (induced by source paper "towards amortized ranking critical training for collaborative filtering"):
  with members:
    - "embarrassingly shallow autoencoders for sparse data"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "deep learning based recommender system a survey and new perspectives"
    - "cgans with projection discriminator"
    - "outer product based neural collaborative filtering"
    - "adversarial personalized ranking for recommendation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "recurrent neural networks with top k gains for session based recommendations"

Connected through hyperedge HE-2 (induced by source paper "boosting item based collaborative filtering via nearly uncoupled random walks"):
  with members:
    - "embarrassingly shallow autoencoders for sparse data"
    - "deep learning based recommender system a survey and new perspectives"
    - "adversarial personalized ranking for recommendation"
    - "deep item based collaborative filtering for top n recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "nais neural attentive item similarity model for recommendation"
    - "self attentive sequential recommendation"
    - "neural collaborative filtering"

Connected through hyperedge HE-3 (induced by source paper "adaptive nonnegative matrix factorization and measure comparisons for recommender systems"):
  with members:
    - "pagerank beyond the web"
    - "a theoretical analysis of ndcg type ranking measures"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

Connected through hyperedge HE-4 (induced by source paper "hybridsvd when collaborative information is not enough"):
  with members:
    - "leveraging high dimensional side information for top n recommendation"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "image based recommendations on styles and substitutes"

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)