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 "guaranteed rank minimization via singular value projection"):
  Abstract: Minimizing the rank of a matrix subject to affine constraints is a fundamental problem with many important applications in machine learning and statistics. In this paper we propose a simple and fast algorithm SVP (Singular Value Projection) for rank minimization with affine constraints (ARMP) and show that SVP recovers the minimum rank solution for affine constraints that satisfy the "restricted isometry property" and show robustness of our method to noise. Our results improve upon a recent breakthrough by Recht, Fazel and Parillo (RFP07) and Lee and Bresler (LB09) in three significant ways: #R##N#1) our method (SVP) is significantly simpler to analyze and easier to implement, #R##N#2) we give recovery guarantees under strictly weaker isometry assumptions #R##N#3) we give geometric convergence guarantees for SVP even in presense of noise and, as demonstrated empirically, SVP is significantly faster on real-world and synthetic problems. #R##N#In addition, we address the practically important problem of low-rank matrix completion (MCP), which can be seen as a special case of ARMP. We empirically demonstrate that our algorithm recovers low-rank incoherent matrices from an almost optimal number of uniformly sampled entries. We make partial progress towards proving exact recovery and provide some intuition for the strong performance of SVP applied to matrix completion by showing a more restricted isometry property. Our algorithm outperforms existing methods, such as those of \cite{RFP07,CR08,CT09,CCS08,KOM09,LB09}, for ARMP and the matrix-completion problem by an order of magnitude and is also significantly more robust to noise.

Includes members:
  Member "robust principal component analysis exact recovery of corrupted low rank matrices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "anomaly detecting and ranking of the cloud computing platform by multi view learning")
  Member "the power of convex relaxation near optimal matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "a regularity lemma and low weight approximators for low degree polynomial threshold functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "degree d chow parameters robustly determine degree d ptfs and algorithmic applications")
  Member "matrix completion from a few entries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "matrix completion with noise":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "guaranteed minimum rank approximation from linear observations by nuclear norm minimization with an ellipsoidal constraint":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "run procrustes run on the convergence of accelerated procrustes flow")
  Member "iterative hard thresholding for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")

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.LG(Machine Learning)