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: "high rank matrix completion and subspace clustering with missing data"
  Abstract: This paper considers the problem of completing a matrix with many missing entries under the assumption that the columns of the matrix belong to a union of multiple low-rank subspaces. This generalizes the standard low-rank matrix completion problem to situations in which the matrix rank can be quite high or even full rank. Since the columns belong to a union of subspaces, this problem may also be viewed as a missing-data version of the subspace clustering problem. Let X be an n x N matrix whose (complete) columns lie in a union of at most k subspaces, each of rank  > kn. The main result of the paper shows that under mild assumptions each column of X can be perfectly recovered with high probability from an incomplete version so long as at least CrNlog^2(n) entries of X are observed uniformly at random, with C>1 a constant depending on the usual incoherence conditions, the geometrical arrangement of subspaces, and the distribution of columns over the subspaces. The result is illustrated with numerical experiments and an application to Internet distance matrix completion and topology identification.

Connected through hyperedge HE-1 (induced by source paper "glimps a greedy mixed integer approach for super robust matched subspace detection"):
  with members:
    - "deterministic hypothesis generation for robust fitting of multiple structures"
    - "deterministic consensus maximization with biconvex programming"
    - "dual principal component pursuit"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "coherent motion segmentation in moving camera videos using optical flow orientations"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "high dimensional matched subspace detection when data are missing"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-2 (induced by source paper "polynomial matrix completion for missing data imputation and transductive learning"):
  with members:
    - "why are big data matrices approximately low rank"
    - "guaranteed matrix completion via non convex factorization"
    - "adam a method for stochastic optimization"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "sparse subspace clustering algorithm theory and applications"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "mixture matrix completion"):
  with members:
    - "incoherence optimal matrix completion"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "robust subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "sparse subspace clustering algorithm theory and applications"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from a few entries"

Connected through hyperedge HE-4 (induced by source paper "active feature acquisition with supervised matrix completion"):
  with members:
    - "learning with feature evolvable streams"
    - "matrix completion with queries"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "uniqueness of low rank matrix completion by rigidity theory"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"
    - "active learning with statistical models"

Connected through hyperedge HE-5 (induced by source paper "fusion subspace clustering full and incomplete data"):
  with members:
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "guess who rated this movie identifying users through subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "dynamic network cartography advances in network health monitoring"
    - "sparse subspace clustering algorithm theory and applications"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "tensor matched subspace detection"):
  with members:
    - "fourth order tensors with multidimensional discrete transforms"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"

Connected through hyperedge HE-7 (induced by source paper "recommendation via matrix completion using kolmogorov complexity"):
  with members:
    - "the power of convex relaxation near optimal matrix completion"
    - "the similarity metric"

Connected through hyperedge HE-8 (induced by source paper "clustering of data with missing entries using non convex fusion penalties"):
  with members:
    - "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.IT(Information Theory)