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 "unified scalable equivalent formulations for schatten quasi norms"):
  Abstract: The Schatten quasi-norm can be used to bridge the gap between the nuclear norm and rank function, and is the tighter approximation to matrix rank. However, most existing Schatten quasi-norm minimization (SQNM) algorithms, as well as for nuclear norm minimization, are too slow or even impractical for large-scale problems, due to the SVD or EVD of the whole matrix in each iteration. In this paper, we rigorously prove that for any p, p1, p2>0 satisfying 1/p=1/p1+1/p2, the Schatten-p quasi-norm of any matrix is equivalent to minimizing the product of the Schatten-p1 norm (or quasi-norm) and Schatten-p2 norm (or quasi-norm) of its two factor matrices. Then we present and prove the equivalence relationship between the product formula of the Schatten quasi-norm and its weighted sum formula for the two cases of p1 and p2: p1=p2 and p1\neq p2. In particular, when p>1/2, there is an equivalence between the Schatten-p quasi-norm of any matrix and the Schatten-2p norms of its two factor matrices, where the widely used equivalent formulation of the nuclear norm can be viewed as a special case. That is, various SQNM problems with p>1/2 can be transformed into the one only involving smooth, convex norms of two factor matrices, which can lead to simpler and more efficient algorithms than conventional methods. #R##N#We further extend the theoretical results of two factor matrices to the cases of three and more factor matrices, from which we can see that for any 0<p<1, the Schatten-p quasi-norm of any matrix is the minimization of the mean of the Schatten-(p3+1)p norms of all factor matrices, where p3 denotes the largest integer not exceeding 1/p. In other words, for any 0<p<1, the SQNM problem can be transformed into an optimization problem only involving the smooth, convex norms of multiple factor matrices.

Includes members:
  Member "scalable algorithms for tractable schatten quasi norm minimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "matrix completion with deterministic sampling theories and methods")
  Member "generalized higher order tensor decomposition via parallel admm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "structured low rank matrix factorization with missing and grossly corrupted observations")
  Member "low rank matrix recovery from errors and erasures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provable subspace tracking from missing data and matrix completion")
  Member "robust pca via outlier pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "the power of convex relaxation near optimal matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "robust principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)