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: "stability of matrix factorization for collaborative filtering"
  Abstract: We study the stability vis a vis adversarial noise of matrix factorization algorithm for matrix completion. In particular, our results include: (I) we bound the gap between the solution matrix of the factorization method and the ground truth in terms of root mean square error; (II) we treat the matrix factorization as a subspace fitting problem and analyze the difference between the solution subspace and the ground truth; (III) we analyze the prediction error of individual users based on the subspace stability. We apply these results to the problem of collaborative filtering under manipulator attack, which leads to useful insights and guidelines for collaborative filtering system design.

Connected through hyperedge HE-1 (induced by source paper "data poisoning attacks on factorization based collaborative filtering"):
  with members:
    - "is feature selection secure against training data poisoning"
    - "poisoning attacks against support vector machines"
    - "low rank matrix recovery from errors and erasures"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-2 (induced by source paper "practical matrix completion and corruption recovery using proximal alternating robust subspace minimization"):
  with members:
    - "a combinatorial algebraic approach for the identifiability of low rank matrix completion"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "online robust subspace tracking from partial information"
    - "low rank matrix recovery from errors and erasures"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "stable principal component pursuit"
    - "dense error correction for low rank matrices via principal component pursuit"

Connected through hyperedge HE-3 (induced by source paper "fast differentially private matrix factorization"):
  with members:
    - "large scale distributed bayesian matrix factorization using stochastic gradient mcmc"
    - "guess who rated this movie identifying users through subspace clustering"
    - "calibrating data to sensitivity in private data analysis"
    - "bayesian posterior sampling via stochastic gradient fisher scoring"
    - "graphlab a new framework for parallel machine learning"
    - "collaborative filtering in a non uniform world learning with the weighted trace norm"
    - "matrix completion from noisy entries"

Connected through hyperedge HE-4 (induced by source paper "social trust prediction via max norm constrained 1 bit matrix completion"):
  with members:
    - "the power of convex relaxation near optimal matrix completion"
    - "guaranteed rank minimization via singular value projection"
    - "exact matrix completion via convex optimization"

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.NA(Numerical Analysis)