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 "on u statistics and compressed sensing i non asymptotic average case analysis"):
  Abstract: Hoeffding's U-statistics model combinatorial-type matrix parameters (appearing in CS theory) in a natural way. This paper proposes using these statistics for analyzing random compressed sensing matrices, in the non-asymptotic regime (relevant to practice). The aim is to address certain pessimisms of worst-case restricted isometry analyses, as observed by both Blanchard and Dossal,   We show how U-statistics can obtain average-case analyses, by relating to statistical restricted isometry property (StRIP) type recovery guarantees. However unlike standard StRIP, random signal models are not required; the analysis used here holds in the almost sure (probabilistic) sense. For Gaussian/bounded entry matrices, we show that both   $\ell_{1}$  -minimization and LASSO essentially require on the order of   $k \cdot [\log ((n-k)/u) + \sqrt {2(k/n) \log (n/k)}]$   measurements to respectively recover at least    $1-5u$   fraction, and   $1-4u$    fraction, of the signals. Noisy conditions are considered. Empirical evidence suggests our analysis to compare well to Donoho and Tanner's recent large deviation bounds for   $\ell_{0}/\ell_{1}$  -equivalence, in the regime of block lengths    $1000 \sim 3000$   with high undersampling (   $50 \sim 150$   measurements); similar system sizes are found in recent CS implementation. In this work, it is assumed throughout that matrix columns are independently sampled.

Includes members:
  Member "improved bounds on restricted isometry constants for gaussian matrices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices")
  Member "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "frame moments and welch bound with erasures")
  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 "expected rip conditioning of the modulated wideband converter":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analog to digital cognitive radio sampling detection and hardware")

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)