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 "on low rank trace regression under general sampling distribution"):
  Abstract: A growing number of modern statistical learning problems involve estimating a large number of parameters from a (smaller) number of observations. In a subset of these problems (matrix completion, matrix compressed sensing, and multi-task learning) the unknown parameters form a high-dimensional matrix, and two popular approaches for the estimation are trace-norm regularized linear regression or alternating minimization. It is also known that these estimators satisfy certain optimal tail bounds under assumptions on rank, coherence, or spikiness of the unknown matrix. #R##N#We study a general family of estimators and sampling distribution that include the above two estimators, and introduce a general notion of spikiness and rank for the unknown matrix. Next, we extend the existing literature on the analysis of these estimators and provide a unifying technique to prove tail bounds for the estimation error. We demonstrate the benefit of this generalization by studying its application to four problems of (1) matrix completion, (2) multi-task learning, (3) compressed sensing with Gaussian ensembles, and (4) compressed sensing with factored measurements. For (1) and (3), we recover matching tail bounds as those found in the literature, and for (2) and (4) we obtain (to the best of our knowledge) the first tail bounds. #R##N#Our approach relies on a generic recipe to prove restricted strong convexity for the sampling operator of the trace regression, that only requires finding upper bounds on certain norms of the parameter matrix.

Includes members:
  Member "an overview of low rank matrix recovery from incomplete observations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "recovering low rank matrices from few coefficients in any basis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "restricted strong convexity and weighted matrix completion optimal bounds with noise":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "the power of convex relaxation near optimal matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "matrix completion from noisy entries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "matrix completion from a few entries":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "a simpler approach to matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "federated over the air subspace learning from incomplete data")
  Member "matrix completion with noise":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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.LG(Machine Learning)