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: "general deviants an analysis of perturbations in compressed sensing"
  Abstract: We analyze the Basis Pursuit recovery of signals with general perturbations. Previous studies have only considered partially perturbed observations Ax + e. Here, x is a signal which we wish to recover, A is a full-rank matrix with more columns than rows, and e is simple additive noise. Our model also incorporates perturbations E to the matrix A which result in multiplicative noise. This completely perturbed framework extends the prior work of Candes, Romberg, and Tao on stable signal recovery from incomplete and inaccurate measurements. Our results show that, under suitable conditions, the stability of the recovered signal is limited by the noise level in the observation. Moreover, this accuracy is within a constant multiple of the best-case reconstruction using the technique of least squares. In the absence of additive noise, numerical simulations essentially confirm that this error is a linear function of the relative perturbation.

Connected through hyperedge HE-1 (induced by source paper "compressive sensing for cut improvement and local clustering"):
  with members:
    - "statistical guarantees for local graph clustering"
    - "power weighted shortest paths for clustering euclidean data"
    - "flow based local graph clustering with better seed set inclusion"
    - "capacity releasing diffusion for speed and locality"
    - "an optimization approach to locally biased graph algorithms"
    - "a simple and strongly local flow based method for cut improvement"
    - "detecting overlapping communities from local spectral subspaces"
    - "computing heat kernel pagerank and a local clustering algorithm"

Connected through hyperedge HE-2 (induced by source paper "reconstruction under outliers for fourier sparse functions"):
  with members:
    - "compressed sensing with adversarial sparse noise via l1 regression"
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "efficient algorithms for outlier robust regression"
    - "robust polynomial regression up to the information theoretic limit"
    - "fourier sparse interpolation without a frequency gap"
    - "nearly optimal deterministic algorithm for sparse walsh hadamard transform"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "analysis of boolean functions"

Connected through hyperedge HE-3 (induced by source paper "quantum annealing based binary compressive sensing with matrix uncertainty"):
  with members:
    - "bilinear adaptive generalized vector approximate message passing"
    - "extending the d wave with support for higher precision coefficients"
    - "nonnegative binary matrix factorization with a d wave quantum annealer"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "sparsity cognizant total least squares for perturbed compressive sampling"

Connected through hyperedge HE-4 (induced by source paper "atomic norm minimization for modal analysis from random and compressed samples"):
  with members:
    - "sparse methods for direction of arrival estimation"
    - "on the sample complexity of multichannel frequency estimation via convex optimization"
    - "generalized line spectral estimation via convex optimization"
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "distributed compressed sensing off the grid"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "continuous compressed sensing with a single or multiple measurement vectors"

Connected through hyperedge HE-5 (induced by source paper "signal recovery from unlabeled samples"):
  with members:
    - "linear regression without correspondence"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "self calibration and biconvex compressive sensing"
    - "unlabeled sensing with random linear measurements"
    - "timing channels with multiple identical quanta"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "efficient algorithms for outlier robust regression"):
  with members:
    - "learning geometric concepts with nasty noise"
    - "exact tensor completion with sum of squares"
    - "better agnostic clustering via relaxed tensor norms"
    - "mixture models robustness and sum of squares proofs"
    - "learning from untrusted data"
    - "robust estimators in high dimensions without the computational intractability"
    - "robust regression and lasso"

Connected through hyperedge HE-7 (induced by source paper "blind demixing and deconvolution at near optimal rate"):
  with members:
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "recovering structured data from superimposed non linear measurements"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "low rank tensor recovery via iterative hard thresholding"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "self calibration and biconvex compressive sensing"
    - "rip like properties in subsampled blind deconvolution"

Connected through hyperedge HE-8 (induced by source paper "signal recovery in perturbed fourier compressed sensing"):
  with members:
    - "a non convex blind calibration method for randomised sensing strategies"
    - "self calibration via linear least squares"
    - "self calibration and biconvex compressive sensing"
    - "compressive phase retrieval via generalized approximate message passing"
    - "convex optimization approaches for blind sensor calibration using sparsity"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "robustly stable signal recovery in compressed sensing with structured matrix perturbation"
    - "compressed sensing off the grid"

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)