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 "the phase transition of matrix recovery from gaussian measurements matches the minimax mse of matrix denoising"):
  Abstract: Let be an unknown M by N matrix. In matrix recovery, one takes linear measurements of , where and each is an M by N matrix. A popular approach for matrix recovery is nuclear norm minimization (NNM): solving the convex optimization problem for all , where denotes the nuclear norm, namely, the sum of singular values. Empirical work reveals a phase transition curve, stated in terms of the undersampling fraction , rank fraction , and aspect ratio . Specifically when the measurement matrices Ai have independent standard Gaussian random entries, a curve exists such that, if , NNM typically succeeds for large M,N, whereas if , it typically fails. An apparently quite different problem is matrix denoising in Gaussian noise, in which an unknown M by N matrix is to be estimated based on direct noisy measurements , where the matrix Z has independent and identically distributed Gaussian entries. A popular matrix denoising scheme solves the unconstrained optimization problem . When optimally tuned, this scheme achieves the asymptotic minimax mean-squared error , where . We report extensive experiments showing that the phase transition in the first problem, matrix recovery from Gaussian measurements, coincides with the minimax risk curve in the second problem, matrix denoising in Gaussian noise: , for any rank fraction (at each common aspect ratio β). Our experiments considered matrices belonging to two constraint classes: real M by N matrices, of various ranks and aspect ratios, and real symmetric positive-semidefinite N by N matrices, of various ranks.

Includes members:
  Member "recovering low rank matrices from few coefficients in any basis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "simple bounds for recovering low complexity models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the error in phase transition computations for compressed sensing")
  Member "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "living near the edge a lower bound on the phase transition of total variation minimization")
  Member "the dynamics of message passing on dense graphs with applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "generalized approximate message passing for estimation with random linear mixing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "matrix completion from noisy entries":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "message passing algorithms for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "robust principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)