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: "restricted isometry of fourier matrices and list decodability of random linear codes"
  Abstract: We prove that a random linear code over F_q, with probability arbitrarily close to 1, is list decodable at radius (1-1/q-\epsilon) with list size L=O(1/\epsilon^2) and rate R=\Omega_q(\epsilon^2/(log^3(1/\epsilon))). Up to the polylogarithmic factor in (1/\epsilon) and constant factors depending on q, this matches the lower bound L=\Omega_q(1/\epsilon^2) for the list size and upper bound R=O_q(\epsilon^2) for the rate. Previously only existence (and not abundance) of such codes was known for the special case q=2 (Guruswami, H{\aa}stad, Sudan and Zuckerman, 2002). #R##N#In order to obtain our result, we employ a relaxed version of the well known Johnson bound on list decoding that translates the average Hamming distance between codewords to list decoding guarantees. We furthermore prove that the desired average-distance guarantees hold for a code provided that a natural complex matrix encoding the codewords satisfies the Restricted Isometry Property with respect to the Euclidean norm (RIP-2). For the case of random binary linear codes, this matrix coincides with a random submatrix of the Hadamard-Walsh transform matrix that is well studied in the compressed sensing literature. #R##N#Finally, we improve the analysis of Rudelson and Vershynin (2008) on the number of random frequency samples required for exact reconstruction of k-sparse signals of length N. Specifically, we improve the number of samples from O(k log(N) log^2(k) (log k + loglog N)) to O(k log(N) log^3(k)). The proof involves bounding the expected supremum of a related Gaussian process by using an improved analysis of the metric defined by the process. This improvement is crucial for our application in list decoding.

Connected through hyperedge HE-1 (induced by source paper "one shot neural architecture search via compressive sensing"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "exploring randomly wired neural networks for image recognition"
    - "learnable embedding space for efficient neural architecture compression"
    - "evaluating the search phase of neural architecture search"
    - "massively parallel hyperparameter tuning"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-2 (induced by source paper "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"):
  with members:
    - "sparse reconstruction from hadamard matrices a lower bound"
    - "estimating the frequency of a clustered signal"
    - "improved lower bounds for the restricted isometry property of subsampled fourier matrices"
    - "an improved lower bound for sparse reconstruction from subsampled hadamard matrices"
    - "dimension independent sparse fourier transform"
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "active regression via linear sample sparsification"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"

Connected through hyperedge HE-3 (induced by source paper "ldpc codes achieve list decoding capacity"):
  with members:
    - "improved list decodability of random linear binary codes"
    - "improved decoding of folded reed solomon and multiplicity codes"
    - "linear time list recovery of high rate expander codes"
    - "analysis of boolean functions"
    - "every list decodable code for high noise has abundant near optimal rate puncturings"
    - "on the list decodability of random linear codes with large error rates"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "folded codes from function field towers and improved optimal rate list decoding"

Connected through hyperedge HE-4 (induced by source paper "list decoding random euclidean codes and infinite constellations"):
  with members:
    - "good distance lattices from high dimensional expanders"
    - "improved list decodability of random linear binary codes"
    - "quadratically constrained myopic adversarial channels"
    - "on the weight distribution of random binary linear codes"
    - "list decoding with double samplers"
    - "sufficiently myopic adversaries are blind"
    - "it ll probably work out improved list decoding through random operations"
    - "every list decodable code for high noise has abundant near optimal rate puncturings"

Connected through hyperedge HE-5 (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"
    - "average case hardness of rip certification"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "faster johnson lindenstrauss transforms via kronecker products"):
  with members:
    - "oblivious sketching of high degree polynomial kernels"
    - "a practical randomized cp tensor decomposition"
    - "sketching for kronecker product regression and p splines"
    - "optimality of the johnson lindenstrauss lemma"
    - "the restricted isometry property of subsampled fourier matrices"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

Connected through hyperedge HE-7 (induced by source paper "combinatorial list decoding of reed solomon codes beyond the johnson radius"):
  with members:
    - "every list decodable code for high noise has abundant near optimal rate puncturings"
    - "on the list decodability of random linear codes with large error rates"
    - "on the list decodability of random linear codes"
    - "explicit codes achieving list decoding capacity error correction with optimal redundancy"

Connected through hyperedge HE-8 (induced by source paper "improved lower bounds for the restricted isometry property of subsampled fourier matrices"):
  with members:
    - "discrete uncertainty principles and sparse signal processing"

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.IT(Information Theory)