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: "on deterministic sampling patterns for robust low rank matrix completion"
  Abstract: In this letter, we study the deterministic sampling patterns for the completion of low-rank matrix, when corrupted with a sparse noise, also known as robust matrix completion. We extend the recent results on the deterministic sampling patterns in the absence of noise based on the geometric analysis on the Grassmannian manifold. A special case where each column has a certain number of noisy entries is considered, where our probabilistic analysis performs very efficiently. Furthermore, assuming that the rank of the original matrix is not given, we provide an analysis to determine if the rank of a valid completion is indeed the actual rank of the data corrupted with sparse noise by verifying some conditions.

Connected through hyperedge HE-1 (induced by source paper "weighted matrix completion from non random non uniform sampling patterns"):
  with members:
    - "matrix completion with deterministic pattern a geometric perspective"
    - "fundamental conditions for low cp rank tensor completion"
    - "rank determination for low rank data completion"
    - "matrix completion via max norm constrained optimization"
    - "weighted matrix completion and recovery with prior subspace information"
    - "recovery guarantee of weighted low rank approximation via alternating minimization"
    - "mc 2 a two phase algorithm for leveraged matrix completion"
    - "tighter low rank approximation via sampling the leveraged element"

Connected through hyperedge HE-2 (induced by source paper "matrix completion with deterministic pattern a geometric perspective"):
  with members:
    - "fundamental conditions for low cp rank tensor completion"
    - "rank determination for low rank data completion"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "practical sketching algorithms for low rank matrix approximation"
    - "guaranteed matrix completion via non convex factorization"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "incoherence optimal matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"

Connected through hyperedge HE-3 (induced by source paper "background subtraction in real applications challenges current models and future directions"):
  with members:
    - "a comparison between background modelling methods for vehicle segmentation in highway traffic videos"
    - "recognition in terra incognita"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "on the role and the importance of features for background modeling and foreground detection"
    - "towards benchmarking scene background initialization"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "image segmentation in video sequences a probabilistic approach"
    - "support predicted modified cs for recursive robust principal components pursuit"

Connected through hyperedge HE-4 (induced by source paper "deterministic and probabilistic conditions for finite completability of low rank tensor"):
  with members:
    - "fundamental conditions for low cp rank tensor completion"
    - "rank determination for low rank data completion"
    - "low tubal rank tensor completion using alternating minimization"
    - "tensor completion by alternating minimization under the tensor train tt model"
    - "a new convex relaxation for tensor completion"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "phase retrieval via matrix completion"
    - "high rank matrix completion and subspace clustering with missing data"

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)