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 "approximately certifying the restricted isometry property is hard"):
  Abstract: A matrix is said to possess the restricted isometry property (RIP), if it acts as an approximate isometry when restricted to sparse vectors. Previous work has shown it to be NP-hard to determine whether a matrix possess this property, but only in a narrow range of parameters. In this paper, we show that it is NP-hard to make this determination for  any  accuracy parameter, even when we restrict ourselves to instances which are either RIP or far from being RIP. This result implies that it is NP-hard to approximate the range of parameters for which a matrix possesses the RIP with accuracy better than some constant. Ours is the first work to prove such a claim without any additional assumptions.

Includes members:
  Member "average case hardness of rip certification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "derandomized compressed sensing with nonuniform guarantees for 120001 1 recovery")
  Member "the restricted isometry property of subsampled fourier matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "one shot neural architecture search via compressive sensing")
  Member "computational complexity of certifying restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "functional nonlinear sparse models")
  Member "sparse representation of a polytope and recovery of sparse signals and low rank matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the importance of phase in complex compressive sensing")
  Member "sharp rip bound for sparse signal and low rank matrix recovery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "hidden cliques and the certification of the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the average case complexity of counting cliques in erdos renyi hypergraphs")
  Member "compressed sensing how sharp is the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices")

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.CC(Computational Complexity)