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 "localization from incomplete euclidean distance matrix performance analysis for the svd mds approach"):
  Abstract: Localizing a cloud of points from noisy measurements of a subset of pairwise distances has applications in various areas, such as sensor network localization and reconstruction of protein conformations from nuclear magnetic resonance measurements. Drineas  et al.  proposed a natural two-stage approach, named singular value decomposition (SVD)–multidimensional scaling (MDS), for this purpose at the 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks. This approach consists of a low-rank matrix completion algorithm, named SVD-reconstruct, to estimate random missing distances, and the classic MDS method to estimate the positions of nodes. In this paper, we present a detailed analysis for this method. More specifically, we first establish error bounds for  Euclidean distance matrix  completion in both expectation and tail forms. Utilizing these results, we then derive the error bound for the recovered positions of nodes. In order to assess the performance of SVD-reconstruct, we present the minimax lower bound of the zero-diagonal, symmetric, low-rank matrix completion problem by Fano's method. This result reveals that when the noise level is low, the SVD-reconstruct approach for Euclidean distance matrix completion is suboptimal in the minimax sense; when the noise level is high, SVD-reconstruct can achieve the optimal rate up to a constant factor.

Includes members:
  Member "calibration using matrix completion with application to ultrasound tomography":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "euclidean distance matrices essential theory algorithms and applications")
  Member "restricted strong convexity and weighted matrix completion optimal bounds with noise":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "matrix completion from noisy entries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")

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)