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: "dense error correction for low rank matrices via principal component pursuit"
  Abstract: We consider the problem of recovering a low-rank matrix when some of its entries, whose locations are not known a priori, are corrupted by errors of arbitrarily large magnitude. It has recently been shown that this problem can be solved efficiently and effectively by a convex program named Principal Component Pursuit (PCP), provided that the fraction of corrupted entries and the rank of the matrix are both sufficiently small. In this paper, we extend that result to show that the same convex program, with a slightly improved weighting parameter, exactly recovers the low-rank matrix even if "almost all" of its entries are arbitrarily corrupted, provided the signs of the errors are random. We corroborate our result with simulations on randomly generated matrices and errors.

Connected through hyperedge HE-1 (induced by source paper "on universal features for high dimensional learning and inference"):
  with members:
    - "on the sample complexity of hgr maximal correlation functions"
    - "probabilistic clustering using maximal matrix norm couplings"
    - "an information theoretic framework for non linear canonical correlation analysis"
    - "deep generalized canonical correlation analysis"
    - "strong data processing inequalities for channels and bayesian networks"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "principal inertia components and applications"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-2 (induced by source paper "the projected power method an efficient algorithm for joint alignment from pairwise differences"):
  with members:
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "fast algorithms for robust pca via gradient descent"
    - "f divergence inequalities"
    - "community recovery in graphs with locality"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "matrix completion has no spurious local minimum"
    - "a geometric analysis of phase retrieval"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"

Connected through hyperedge HE-3 (induced by source paper "information recovery from pairwise measurements"):
  with members:
    - "community recovery in graphs with locality"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "information recovery from pairwise measurements a shannon theoretic approach"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "bounds among f divergences"
    - "discrete signal processing on graphs sampling theory"
    - "optimal haplotype assembly from high throughput mate pair reads"
    - "near optimal joint object matching via convex relaxation"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "sparsity aware learning and compressed sensing an overview"):
  with members:
    - "experiments with random projection"
    - "compressive principal component pursuit"
    - "stable image reconstruction using total variation minimization"
    - "recovering low rank matrices from few coefficients in any basis"
    - "online sparse system identification and signal reconstruction using projections onto weighted ell_ 1 balls"
    - "structured compressed sensing from theory to applications"
    - "performance analysis of sparse recovery based on constrained minimal singular values"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"

Connected through hyperedge HE-6 (induced by source paper "robust pca with partial subspace knowledge"):
  with members:
    - "a correctness result for online robust pca"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "prost a smoothed lp norm robust online subspace tracking method for realtime background subtraction in video"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "compressive principal component pursuit"
    - "principal component pursuit with reduced linear measurements"
    - "recovering low rank matrices from few coefficients in any basis"

Connected through hyperedge HE-7 (induced by source paper "cauchy principal component analysis"):
  with members:
    - "stable principal component pursuit"
    - "robust pca via outlier pursuit"
    - "guaranteed rank minimization via singular value projection"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "low rank structure learning via nonconvex heuristic recovery"):
  with members:
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "stable principal component pursuit"
    - "robust principal component analysis exact recovery of corrupted low rank matrices"
    - "robust principal component analysis"
    - "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)