human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "weighted matrix completion from non random non uniform sampling patterns"):
  Abstract: We study the matrix completion problem when the observation pattern is deterministic and possibly non-uniform. We propose a simple and efficient debiased projection scheme for recovery from noisy observations and analyze the error under a suitable weighted metric. We introduce a simple function of the weight matrix and the sampling pattern that governs the accuracy of the recovered matrix. We derive theoretical guarantees that upper bound the recovery error and nearly matching lower bounds that showcase optimality in several regimes. Our numerical experiments demonstrate the computational efficiency and accuracy of our approach, and show that debiasing is essential when using non-uniform sampling patterns.

Includes members:
  Member "matrix completion with deterministic pattern a geometric perspective":
    also connected through other hyperedges:
      (none)
  Member "on deterministic sampling patterns for robust low rank matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "matrix completion with deterministic pattern a geometric perspective")
  Member "fundamental conditions for low cp rank tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data")
  Member "rank determination for low rank data completion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi dimensional spectral super resolution with prior knowledge via frequency selective vandermonde decomposition and admm")
  Member "matrix completion via max norm constrained optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "binary matrix completion with nonconvex regularizers")
  Member "weighted matrix completion and recovery with prior subspace information":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "recovery guarantee of weighted low rank approximation via alternating minimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regularized weighted low rank approximation")
  Member "mc 2 a two phase algorithm for leveraged matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weighted matrix completion and recovery with prior subspace information")

Question: Please tell me which class the source paper 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)