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: "average case hardness of rip certification"
  Abstract: The restricted isometry property (RIP) for design matrices gives guarantees for optimal recovery in sparse linear models. It is of high interest in compressed sensing and statistical learning. This property is particularly important for computationally efficient recovery methods. As a consequence, even though it is in general NP-hard to check that RIP holds, there have been substantial efforts to find tractable proxies for it. These would allow the construction of RIP matrices and the polynomial-time verification of RIP given an arbitrary matrix. We consider the framework of average-case certifiers, that never wrongly declare that a matrix is RIP, while being often correct for random instances. While there are such functions which are tractable in a suboptimal parameter regime, we show that this is a computationally hard task in any better regime. Our results are based on a new, weaker assumption on the problem of detecting dense subgraphs.

Connected through hyperedge HE-1 (induced by source paper "derandomized compressed sensing with nonuniform guarantees for 120001 1 recovery"):
  with members:
    - "kesten mckay law for random subensembles of paley equiangular tight frames"
    - "random subsets of structured deterministic frames have manova spectra"
    - "the restricted isometry property of subsampled fourier matrices"
    - "living on the edge phase transitions in convex programs with random data"
    - "compressed sensing with sparse binary matrices instance optimal error guarantees in near optimal time"
    - "kirkman equiangular tight frames and codes"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"

Connected through hyperedge HE-2 (induced by source paper "approximately certifying the restricted isometry property is hard"):
  with members:
    - "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"
    - "hidden cliques and the certification of the restricted isometry property"
    - "compressed sensing how sharp is the restricted isometry property"

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.LG(Machine Learning)