human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on the theorem of uniform recovery of random sampling matrices"
  Abstract: We consider two theorems from the theory of compressive sensing. Mainly a theorem concerning uniform recovery of random sampling matrices, where the number of samples needed in order to recover an $s$-sparse signal from linear measurements (with high probability) is known to be $m\gtrsim s(\ln s)^3\ln N$. We present new and improved constants together with what we consider to be a more explicit proof. A proof that also allows for a slightly larger class of $m\times N$-matrices, by considering what we call \emph{low entropy}. We also present an improved condition on the so-called restricted isometry constants, $\delta_s$, ensuring sparse recovery via $\ell^1$-minimization. We show that $\delta_{2s}<4/\sqrt{41}$ is sufficient and that this can be improved further to almost allow for a sufficient condition of the type $\delta_{2s}<2/3$.

Connected through hyperedge HE-1 (induced by source paper "matrix infinitely divisible series tail inequalities and applications in optimization"):
  with members:
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "from random matrix theory to coding theory volume of a metric ball in unitary group"
    - "robust estimates of covariance matrices in the large dimensional regime"
    - "improved bounds for the nystrom method with application to kernel classification"

Connected through hyperedge HE-2 (induced by source paper "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"):
  with members:
    - "uniform recovery from subgaussian multi sensor measurements"
    - "compressed sensing and parallel acquisition"
    - "sparsity and parallel acquisition optimal uniform and nonuniform recovery guarantees"
    - "optimal sparse recovery for multi sensor measurements"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "refined analysis of sparse mimo radar"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"

Connected through hyperedge HE-3 (induced by source paper "weighted ell_1 minimization for sparse recovery under arbitrary prior information"):
  with members:
    - "compressed sensing with prior information optimal strategies geometry and bounds"
    - "recovery analysis for weighted ell_1 minimization using a null space property"
    - "exact recovery for sparse signal via weighted l_1 minimization"
    - "weighted ell_1 minimization with multiple weighting sets"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "recovering compressively sampled signals using partial support information"
    - "model based compressive sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-4 (induced by source paper "recovery of signals with low density"):
  with members:
    - "relaxed recovery conditions for omp ols by exploiting both coherence and decay"
    - "uncertainty relations and sparse signal recovery for pairs of general signal sets"
    - "performance analysis of sparse recovery based on constrained minimal singular values"
    - "recovery of sparsely corrupted signals"

Connected through hyperedge HE-5 (induced by source paper "on the absence of the rip in real world applications of compressed sensing and the rip in levels"):
  with members:
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "an analysis of block sampling strategies in compressed sensing"
    - "communications inspired projection design with application to compressive sensing"
    - "stable and robust sampling strategies for compressive imaging"
    - "on variable density compressive sampling"
    - "model based compressive sensing"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-6 (induced by source paper "sparse representation of a polytope and recovery of sparse signals and low rank matrices"):
  with members:
    - "compressed sensing and affine rank minimization under restricted isometry"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-7 (induced by source paper "exact recovery for sparse signal via weighted l_1 minimization"):
  with members:
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "recovering compressively sampled signals using partial support information"
    - "new bounds for restricted isometry constants"

Question: Please tell me which class the center node 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)