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: "hidden cliques and the certification of the restricted isometry property"
  Abstract: Compressed sensing is a technique for finding sparse solutions to underdetermined linear systems. This technique relies on properties of the sensing matrix such as the restricted isometry property. Sensing matrices that satisfy this property with optimal parameters are mainly obtained via probabilistic arguments. Deciding whether a given matrix satisfies the restricted isometry property is a non-trivial computational problem. Indeed, we show in this paper that restricted isometry parameters cannot be approximated in polynomial time within any constant factor under the assumption that the hidden clique problem is hard. Moreover, on the positive side we propose an improvement on the brute-force enumeration algorithm for checking the restricted isometry property.

Connected through hyperedge HE-1 (induced by source paper "the average case complexity of counting cliques in erdos renyi hypergraphs"):
  with members:
    - "optimal average case reductions to sparse pca from weak assumptions to strong hardness"
    - "reducibility and computational lower bounds for problems with planted sparse structure"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "on independent sets in random graphs"
    - "incoherence optimal matrix completion"
    - "nuclear norm minimization for the planted clique and biclique problems"

Connected through hyperedge HE-2 (induced by source paper "on the restricted isometry of the columnwise khatri rao product"):
  with members:
    - "graph sampling for covariance estimation"
    - "on the support recovery of jointly sparse gaussian sources using sparse bayesian learning"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "smoothed analysis of tensor decompositions"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-3 (induced by source paper "approximately certifying the restricted isometry property is hard"):
  with members:
    - "average case hardness of rip certification"
    - "the restricted isometry property of subsampled fourier matrices"
    - "computational complexity of certifying restricted isometry property"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "compressed sensing how sharp is the restricted isometry property"

Connected through hyperedge HE-4 (induced by source paper "a nearly tight sum of squares lower bound for the planted clique problem"):
  with members:
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "sum of squares lower bounds for sparse pca"
    - "sum of squares lower bounds from pairwise independence"
    - "sos and planted clique tight analysis of mpw moments at all degrees and an optimal lower bound at degree four"
    - "sum of squares lower bounds for planted clique"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "rounding sum of squares relaxations"

Connected through hyperedge HE-5 (induced by source paper "average case hardness of rip certification"):
  with members:
    - "label optimal regret bounds for online local learning"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"
    - "optimal solutions for sparse principal component analysis"
    - "noise tolerant learning the parity problem and the statistical query model"

Connected through hyperedge HE-6 (induced by source paper "incoherence optimal matrix completion"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "spectral compressed sensing via structured matrix completion"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "recovering low rank matrices from few coefficients in any basis"
    - "low rank matrix recovery from errors and erasures"
    - "note on sampling without replacing from a finite collection of matrices"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from a few entries"

Connected through hyperedge HE-7 (induced by source paper "l_ 1 2 regularization convergence of iterative half thresholding algorithm"):
  with members:
    - "compressed sensing and affine rank minimization under restricted isometry"
    - "structured compressed sensing from theory to applications"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "computational complexity of certifying restricted isometry property"):
  with members:
    - "on the certification of the restricted isometry property"
    - "reductions between expansion problems"

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.CC(Computational Complexity)