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: "link prediction via matrix completion"
  Abstract: Inspired by the practical importance of social networks, economic networks, biological networks and so on, studies on large and complex networks have attracted a surge of attention in the recent years. Link prediction is a fundamental issue to understand the mechanisms by which new links are added to the networks. We introduce the method of robust principal component analysis (robust PCA) into link prediction, and estimate the missing entries of the adjacency matrix. On the one hand, our algorithm is based on the sparse and low-rank property of the matrix, while, on the other hand, it also performs very well when the network is dense. This is because a relatively dense real network is also sparse in comparison to the complete graph. According to extensive experiments on real networks from disparate fields, when the target network is connected and sufficiently dense, whether it is weighted or unweighted, our method is demonstrated to be very effective and with prediction accuracy being considerably improved compared to many state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "binary matrix completion with nonconvex regularizers"):
  with members:
    - "positive unlabeled learning with non negative risk estimator"
    - "large scale low rank matrix learning with nonconvex regularizers"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "matrix completion via max norm constrained optimization"
    - "semi supervised classification based on classification from positive and unlabeled data"
    - "network topology inference from spectral templates"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "social trust prediction via max norm constrained 1 bit matrix completion"

Connected through hyperedge HE-2 (induced by source paper "network reconstruction and controlling based on structural regularity analysis"):
  with members:
    - "network structure from rich but noisy data"
    - "predicting drug target interactions via sparse learning"
    - "estimation of graphlet statistics"
    - "higher order organization of complex networks"
    - "robust principal component analysis"

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.SI(Social and Information Networks)