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 "performance analysis of sparse recovery based on constrained minimal singular values"):
  Abstract: The stability of sparse signal reconstruction with respect to measurement noise is investigated in this paper. We design efficient algorithms to verify the sufficient condition for unique l1 sparse recovery. One of our algorithms produces comparable results with the state-of-the-art technique and performs orders of magnitude faster. We show that the l1 -constrained minimal singular value (l1-CMSV) of the measurement matrix determines, in a very concise manner, the recovery performance of l1-based algorithms such as the Basis Pursuit, the Dantzig selector, and the LASSO estimator. Compared to performance analysis involving the Restricted Isometry Constant, the arguments in this paper are much less complicated and provide more intuition on the stability of sparse signal recovery. We show also that, with high probability, the subgaussian ensemble generates measurement matrices with l1-CMSVs bounded away from zero, as long as the number of measurements is relatively large. To compute the l1-CMSV and its lower bound, we design two algorithms based on the interior point algorithm and the semidefinite relaxation.

Includes members:
  Member "sparse volterra and polynomial regression models recoverability and estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nonlinear function estimation with empirical bayes and approximate message passing")
  Member "tight oracle bounds for low rank matrix recovery from a minimal number of random measurements":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "provable meta learning of linear representations")
  Member "performance analysis for sparse support recovery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sample measurement tradeoff in support recovery under a subgaussian prior")
  Member "subspace pursuit for compressive sensing signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "a direct formulation for sparse pca using semidefinite programming":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "supervised quantile normalization for low rank matrix factorization")

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)