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: "computational complexity of certifying restricted isometry property"
  Abstract: Given a matrix $A$ with $n$ rows, a number $k<n$, and $0<\delta < 1$, $A$ is $(k,\delta)$-RIP (Restricted Isometry Property) if, for any vector $x \in \mathbb{R}^n$, with at most $k$ non-zero co-ordinates, $$(1-\delta) \|x\|_2 \leq \|A x\|_2 \leq (1+\delta)\|x\|_2$$ In many applications, such as compressed sensing and sparse recovery, it is desirable to construct RIP matrices with a large $k$ and a small $\delta$. Given the efficacy of random constructions in generating useful RIP matrices, the problem of certifying the RIP parameters of a matrix has become important. #R##N#In this paper, we prove that it is hard to approximate the RIP parameters of a matrix assuming the Small-Set-Expansion-Hypothesis. Specifically, we prove that for any arbitrarily large constant $C>0$ and any arbitrarily small constant $0<\delta<1$, there exists some $k$ such that given a matrix $M$, it is SSE-Hard to distinguish the following two cases: #R##N#- (Highly RIP) $M$ is $(k,\delta)$-RIP. #R##N#- (Far away from RIP) $M$ is not $(k/C, 1-\delta)$-RIP. #R##N#Most of the previous results on the topic of hardness of RIP certification only hold for certification when $\delta=o(1)$. In practice, it is of interest to understand the complexity of certifying a matrix with $\delta$ being close to $\sqrt{2}-1$, as it suffices for many real applications to have matrices with $\delta = \sqrt{2}-1$. Our hardness result holds for any constant $\delta$. Specifically, our result proves that even if $\delta$ is indeed very small, i.e. the matrix is in fact \emph{strongly RIP}, certifying that the matrix exhibits \emph{weak RIP} itself is SSE-Hard. #R##N#In order to prove the hardness result, we prove a variant of the Cheeger's Inequality for sparse vectors.

Connected through hyperedge HE-1 (induced by source paper "functional nonlinear sparse models"):
  with members:
    - "learning power spectrum maps from quantized power measurements"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "block iterative reweighted algorithms for super resolution of spectrally sparse signals"
    - "forward backward greedy algorithms for atomic norm regularization"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "atomic norm denoising with applications to line spectral estimation"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "the restricted isometry property of subsampled fourier matrices"):
  with members:
    - "the list decoding size of fourier sparse boolean functions"
    - "discrete uncertainty principles and sparse signal processing"
    - "on model based rip 1 matrices"
    - "on the list decodability of random linear codes with large error rates"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "new constructions of rip matrices with fast multiplication and fewer rows"
    - "new and improved johnson lindenstrauss embeddings via 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.CC(Computational Complexity)