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 "compressed sensing of block sparse positive vectors"):
  Abstract: In this paper we revisit one of the classical problems of compressed sensing. Namely, we consider linear under-determined systems with sparse solutions. A substantial success in mathematical characterization of an $\ell_1$ optimization technique typically used for solving such systems has been achieved during the last decade. Seminal works \cite{CRT,DOnoho06CS} showed that the $\ell_1$ can recover a so-called linear sparsity (i.e. solve systems even when the solution has a sparsity linearly proportional to the length of the unknown vector). Later considerations \cite{DonohoPol,DonohoUnsigned} (as well as our own ones \cite{StojnicCSetam09,StojnicUpper10}) provided the precise characterization of this linearity. In this paper we consider the so-called structured version of the above sparsity driven problem. Namely, we view a special case of sparse solutions, the so-called block-sparse solutions. Typically one employs $\ell_2/\ell_1$-optimization as a variant of the standard $\ell_1$ to handle block-sparse case of sparse solution systems. We considered systems with block-sparse solutions in a series of work \cite{StojnicCSetamBlock09,StojnicUpperBlock10,StojnicICASSP09block,StojnicJSTSP09} where we were able to provide precise performance characterizations if the $\ell_2/\ell_1$-optimization similar to those obtained for the standard $\ell_1$ optimization in \cite{StojnicCSetam09,StojnicUpper10}. Here we look at a similar class of systems where on top of being block-sparse the unknown vectors are also known to have components of the same sign. In this paper we slightly adjust $\ell_2/\ell_1$-optimization to account for the known signs and provide a precise performance characterization of such an adjustment.

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 "towards a better compressed sensing")
  Member "regularly random duality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the squared error of generalized lasso a precise analysis")
  Member "lifting l 1 optimization strong and sectional thresholds":
    also connected through other hyperedges:
      (none)
  Member "block sparse signals uncertainty relations and efficient recovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dr2 net deep residual reconstruction network for image compressive sensing")
  Member "theoretical and empirical results for recovery from multiple measurements":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the restricted isometry property of block diagonal matrices for group sparse signal recovery")
  Member "model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compressed sensing with invertible generative models and dependent noise")
  Member "weighted ell_1 minimization for sparse recovery with prior information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deterministic analysis of weighted bpdn with partially known support information")

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)