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: "dropout training of matrix factorization and autoencoder for link prediction in sparse graphs"
  Abstract: Matrix factorization (MF) and Autoencoder (AE) are among the most successful approaches of unsupervised learning. While MF based models have been extensively exploited in the graph modeling and link prediction literature, the AE family has not gained much attention. In this paper we investigate both MF and AE's application to the link prediction problem in sparse graphs. We show the connection between AE and MF from the perspective of multiview learning, and further propose MF+AE: a model training MF and AE jointly with shared parameters. We apply dropout to training both the MF and AE parts, and show that it can significantly prevent overfitting by acting as an adaptive regularization. We conduct experiments on six real world sparse graph datasets, and show that MF+AE consistently outperforms the competing methods, especially on datasets that demonstrate strong non-cohesive structures.

Connected through hyperedge HE-1 (induced by source paper "on dropout and nuclear norm regularization"):
  with members:
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "on the implicit bias of dropout"
    - "gradient descent aligns the layers of deep linear networks"
    - "on the relationship between dropout and equiangular tight frames"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "a comprehensive analysis of deep regression"

Connected through hyperedge HE-2 (induced by source paper "on the implicit bias of dropout"):
  with members:
    - "how to escape saddle points efficiently"
    - "structured low rank matrix factorization global optimality algorithms and applications"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "a geometric analysis of phase retrieval"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "on the inductive bias of dropout"

Connected through hyperedge HE-3 (induced by source paper "predicting multicellular function through multi layer tissue networks"):
  with members:
    - "gated graph sequence neural networks"
    - "line large scale information network embedding"
    - "data fusion by matrix factorization"
    - "how transferable are features in deep neural networks"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"
    - "using the gene ontology hierarchy when predicting gene function"

Connected through hyperedge HE-4 (induced by source paper "an analysis of dropout for matrix factorization"):
  with members:
    - "global optimality in tensor factorization deep learning and beyond"
    - "on the inductive bias of dropout"
    - "convex relaxations of structured matrix factorizations"
    - "adding noise to the input of a model trained with a regularized objective"
    - "convex sparse matrix factorizations"

Connected through hyperedge HE-5 (induced by source paper "dropout as a low rank regularizer for matrix factorization"):
  with members:
    - "adam a method for stochastic optimization"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "adding noise to the input of a model trained with a regularized objective"

Connected through hyperedge HE-6 (induced by source paper "node2vec scalable feature learning for networks"):
  with members:
    - "gated graph sequence neural networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "representation learning a review and new perspectives"
    - "supervised random walks predicting and recommending links in social networks"

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.LG(Machine Learning)