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: "an estimation theoretic approach for sparsity pattern recovery in the noisy setting"
  Abstract: Compressed sensing deals with the reconstruction of sparse signals using a small number of linear measurements. One of the main challenges in compressed sensing is to find the support of a sparse signal. In the literature, several bounds on the scaling law of the number of measurements for successful support recovery have been derived where the main focus is on random Gaussian measurement matrices. In this paper, we investigate the noisy support recovery problem from an estimation theoretic point of view, where no specific assumption is made on the underlying measurement matrix. The linear measurements are perturbed by additive white Gaussian noise. We define the output of a support estimator to be a set of position values in increasing order. We set the error between the true and estimated supports as the $\ell_2$-norm of their difference. On the one hand, this choice allows us to use the machinery behind the $\ell_2$-norm error metric and on the other hand, converts the support recovery into a more intuitive and geometrical problem. First, by using the Hammersley-Chapman-Robbins (HCR) bound, we derive a fundamental lower bound on the performance of any \emph{unbiased} estimator of the support set. This lower bound provides us with necessary conditions on the number of measurements for reliable $\ell_2$-norm support recovery, which we specifically evaluate for uniform Gaussian measurement matrices. Then, we analyze the maximum likelihood estimator and derive conditions under which the HCR bound is achievable. This leads us to the number of measurements for the optimum decoder which is sufficient for reliable $\ell_2$-norm support recovery. Using this framework, we specifically evaluate sufficient conditions for uniform Gaussian measurement matrices.

Connected through hyperedge HE-1 (induced by source paper "sub linear time support recovery for compressed sensing using sparse graph codes"):
  with members:
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "sho fa robust compressive sensing with order optimal complexity measurements and bits"
    - "summary based structures with improved sublinear recovery for compressed sensing"
    - "phase retrieval via matrix completion"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "optimal phase transitions in compressed sensing"
    - "information theoretic bounds for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "on the performance bound of sparse estimation with sensing matrix perturbation"):
  with members:
    - "perturbation analysis of orthogonal matching pursuit"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "proof of convergence and performance analysis for sparse recovery via zero point attracting projection"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "noise folding in compressed sensing"
    - "how well can we estimate a sparse vector"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "compressed sensing how sharp is the restricted isometry property"

Connected through hyperedge HE-3 (induced by source paper "sharp support recovery from noisy random measurements by l1 minimization"):
  with members:
    - "approximate sparsity pattern recovery information theoretic lower bounds"
    - "information theoretic bounds for compressed sensing"
    - "necessary and sufficient conditions for sparsity pattern recovery"
    - "sharp sufficient conditions on exact sparsity pattern recovery"
    - "thresholded basis pursuit an lp algorithm for achieving optimal support recovery for sparse and approximately sparse signals from noisy random measurements"
    - "shannon theoretic limits on noisy compressive sampling"
    - "consistency of the group lasso and multiple kernel learning"

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)