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 "towards a better compressed sensing"):
  Abstract: In this paper we look at a well known linear inverse problem that is one of the mathematical cornerstones of the compressed sensing field. In seminal works \cite{CRT,DOnoho06CS} $\ell_1$ optimization and its success when used for recovering sparse solutions of linear inverse problems was considered. Moreover, \cite{CRT,DOnoho06CS} established for the first time in a statistical context that an unknown vector of linear sparsity can be recovered as a known existing solution of an under-determined linear system through $\ell_1$ optimization. In \cite{DonohoPol,DonohoUnsigned} (and later in \cite{StojnicCSetam09,StojnicUpper10}) the precise values of the linear proportionality were established as well. While the typical $\ell_1$ optimization behavior has been essentially settled through the work of \cite{DonohoPol,DonohoUnsigned,StojnicCSetam09,StojnicUpper10}, we in this paper look at possible upgrades of $\ell_1$ optimization. Namely, we look at a couple of algorithms that turn out to be capable of recovering a substantially higher sparsity than the $\ell_1$. However, these algorithms assume a bit of "feedback" to be able to work at full strength. This in turn then translates the original problem of improving upon $\ell_1$ to designing algorithms that would be able to provide output needed to feed the $\ell_1$ upgrades considered in this papers.

Includes members:
  Member "lifting l q optimization thresholds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "does ell_p minimization outperform ell_1 minimization")
  Member "optimality of ell_2 ell_1 optimization block length dependent thresholds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressed sensing of block sparse positive vectors")
  Member "a rigorous geometry probability equivalence in characterization of ell_1 optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "critical behavior and universality classes for an algorithmic phase transition in sparse reconstruction")
  Member "meshes that trap random subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "precise error analysis of regularized m estimators in high dimensions")
  Member "improved bounds on restricted isometry constants for gaussian matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "compressed sensing how sharp is the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices")
  Member "modified cs modifying compressive sensing for problems with partially known support":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multilinear compressive learning with prior knowledge")
  Member "various thresholds for ell_1 optimization in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")

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)