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 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 the restricted isometry property with optimal parameters are mainly obtained via probabilistic arguments. Given any matrix, deciding whether it satisfies the restricted isometry property is a non-trivial computational problem. In this paper, we give reductions from dense subgraph problems to the certification of the restricted isometry property. This gives evidence that certifying the restricted isometry property is unlikely to be feasible in polynomial-time. Moreover, on the positive side we propose an improvement on the brute-force enumeration algorithm for checking the restricted isometry property. #R##N#Another contribution of independent interest is a spectral algorithm for certifying that a random graph does not contain any dense k-subgraph. This "skewed spectral algorithm" performs better than the basic spectral algorithm in a certain range of parameters.

Connected through hyperedge HE-1 (induced by source paper "computational complexity of certifying restricted isometry property"):
  with members:
    - "hidden cliques and the certification of the restricted isometry property"
    - "reductions between expansion problems"

Connected through hyperedge HE-2 (induced by source paper "hidden cliques and the certification of the restricted isometry property"):
  with members:
    - "optimal solutions for sparse principal component analysis"

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)