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 "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"):
  Abstract: The performance of regularized least-squares estimation in noisy compressed sensing is analyzed in the limit when the dimensions of the measurement matrix grow large. The sensing matrix is consider ...

Includes members:
  Member "isotropically random orthogonal matrices performance of lasso and minimum conic singular values":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "precise error analysis of regularized m estimators in high dimensions")
  Member "on the performance of turbo signal recovery with partial dft sensing matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "structured turbo compressed sensing for downlink massive mimo ofdm channel estimation")
  Member "inference for generalized linear models via alternating directions and bethe free energy minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an approximate message passing framework for side information")
  Member "s amp approximate message passing for general matrix ensembles":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "signal recovery using expectation consistent approximation for linear observations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "macroscopic analysis of vector approximate message passing in a model mismatch setting")
  Member "on sparse vector recovery performance in structurally orthogonal matrices via lasso":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse vector recovery bernoulli gaussian message passing")
  Member "asymptotic mmse analysis under sparse representation modeling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "understanding phase transitions via mutual information and mmse")
  Member "statistical mechanics approach to sparse noise denoising":
    also connected through other hyperedges:
      (none)

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)