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 "matrix factorization for nonparametric multi source localization exploiting unimodal properties"):
  Abstract: Herein, the problem of simultaneous localization of multiple sources given a number of energy samples at different locations is examined. The strategies do not require knowledge of the signal propagation models, nor do they exploit the spatial signatures of the source. A nonparametric source localization framework based on a matrix observation model is developed. It is shown that the source location can be estimated by localizing the peaks of a pair of location signature vectors extracted from the incomplete energy observation matrix. A robust peak localization algorithm is developed and shown to decrease the source localization mean squared error (MSE) faster than O(1/M^1.5) with M samples. To extract the source signature vectors from a matrix with mixed energy from multiple sources, a unimodal-constrained matrix factorization (UMF) problem is formulated, and two rotation techniques are developed to solve the UMF efficiently. Our numerical experiments demonstrate that the proposed scheme achieves similar performance as the kernel regression baseline using only 1/5 energy measurement samples in detecting a single source, and the performance gain is more significant in the cases of detecting multiple sources.

Includes members:
  Member "rotated eigenstructure analysis for source localization without energy decay models":
    also connected through other hyperedges:
      (none)
  Member "active target localization using low rank matrix completion and unimodal regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rotated eigenstructure analysis for source localization without energy decay models")
  Member "super resolution of complex exponentials from modulations with unknown waveforms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "resolution limit for line spectral estimation theory and algorithm")
  Member "off the grid line spectrum denoising and estimation with multiple measurement vectors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "blind deconvolution using convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "compressed sensing off the grid":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "matrix completion with noise":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "exact matrix completion via convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

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)