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 "optimality of ell_2 ell_1 optimization block length dependent thresholds"):
  Abstract: The recent work of \cite{CRT,DonohoPol} rigorously proved (in a large dimensional and statistical context) that if the number of equations (measurements in the compressed sensing terminology) in the system is proportional to the length of the unknown vector then there is a sparsity (number of non-zero elements of the unknown vector) also proportional to the length of the unknown vector such that $\ell_1$-optimization algorithm succeeds in solving the system. In more recent papers \cite{StojnicCSetamBlock09,StojnicICASSP09block,StojnicJSTSP09} we considered under-determined systems with the so-called \textbf{block}-sparse solutions. In a large dimensional and statistical context in \cite{StojnicCSetamBlock09} we determined lower bounds on the values of allowable sparsity for any given number (proportional to the length of the unknown vector) of equations such that an $\ell_2/\ell_1$-optimization algorithm succeeds in solving the system. These lower bounds happened to be in a solid numerical agreement with what one can observe through numerical experiments. Here we derive the corresponding upper bounds. Moreover, the upper bounds that we obtain in this paper match the lower bounds from \cite{StojnicCSetamBlock09} and ultimately make them optimal.

Includes members:
  Member "block sparse signals uncertainty relations and efficient recovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "the restricted isometry property of block diagonal matrices for group sparse signal recovery")
  Member "weighted ell_1 minimization for sparse recovery with prior information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deterministic analysis of weighted bpdn with partially known support information")
  Member "average case analysis of multichannel sparse recovery using convex relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sample measurement tradeoff in support recovery under a subgaussian prior")
  Member "block length dependent thresholds in block sparse compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "precise error analysis of regularized m estimators in high dimensions")
  Member "sparse recovery of positive signals with minimal expansion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perfect recovery conditions for non negative sparse modeling")
  Member "simultaneously sparse solutions to linear inverse problems with multiple system matrices and a single observation vector":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressed sensing of block sparse positive vectors")

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)