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: "a combinatorial algebraic approach for the identifiability of low rank matrix completion"
  Abstract: In this paper, we review the problem of matrix completion and expose its intimate relations with algebraic geometry, combinatorics and graph theory. We present the first necessary and sufficient combinatorial conditions for matrices of arbitrary rank to be identifiable from a set of matrix entries, yielding theoretical constraints and new algorithms for the problem of matrix completion. We conclude by algorithmically evaluating the tightness of the given conditions and algorithms for practically relevant matrix sizes, showing that the algebraic-combinatoric approach can lead to improvements over state-of-the-art matrix completion methods.

Connected through hyperedge HE-1 (induced by source paper "matrix completion with deterministic sampling theories and methods"):
  with members:
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "incoherence optimal matrix completion"
    - "robust recovery of subspace structures by low rank representation"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-2 (induced by source paper "practical matrix completion and corruption recovery using proximal alternating robust subspace minimization"):
  with members:
    - "stability of matrix factorization for collaborative filtering"
    - "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 "matrix completion with queries"):
  with members:
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "the power of convex relaxation near optimal matrix completion"
    - "uniqueness of low rank matrix completion by rigidity theory"
    - "matrix completion from noisy entries"
    - "matrix completion from a few entries"
    - "a simpler approach to matrix completion"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "lifting for blind deconvolution in random mask imaging identifiability and convex relaxation"):
  with members:
    - "phase retrieval from coded diffraction patterns"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "blind deconvolution using convex programming"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "recovering low rank matrices from few coefficients in any basis"
    - "a simpler approach to matrix completion"
    - "matrix completion with noise"

Connected through hyperedge HE-5 (induced by source paper "the algebraic combinatorial approach for low rank matrix completion"):
  with members:
    - "learning with cross kernels and ideal pca"
    - "coherence and sufficient sampling densities for reconstruction in compressed sensing"
    - "recovering low rank matrices from few coefficients in any basis"
    - "concentration based guarantees for low rank matrix reconstruction"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "collaborative filtering in a non uniform world learning with the weighted trace norm"
    - "the power of convex relaxation near optimal matrix completion"
    - "uniqueness of low rank matrix completion by rigidity theory"

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)