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: "sparse recovery from nonlinear measurements with applications in bad data detection for power networks"
  Abstract: In this paper, we consider the problem of sparse recovery from nonlinear measurements, which has applications in state estimation and bad data detection for power networks. An iterative mixed l1 and l2 convex programming is used to estimate the true state by locally linearizing the nonlinear measurements. When the measurements are linear, through using the almost Euclidean property for a linear subspace, we derive a new performance bound for the state estimation error under sparse bad data and additive observation noise. When the measurements are nonlinear, we give conditions under which the solution of the iterative algorithm converges to the true state even tho ugh the locally linearized measurements may not be the actual nonlinear measurements. We also numerically evaluate an iterative convex programming approach to perform bad data detections in nonlinear electrical power networks problems. As a byproduct, in this paper we provide sharp bounds on the almost Euclidean property of a linear subspace, using the “escape-through-a-mesh” theorem from geometric functional analysis.

Connected through hyperedge HE-1 (induced by source paper "outlier robust extreme learning machine for multi target regression"):
  with members:
    - "a convex formulation for learning task relationships in multi task learning"
    - "multi target regression via input space expansion treating targets as inputs"

Connected through hyperedge HE-2 (induced by source paper "recursive recovery of sparse signal sequences from compressive measurements a review"):
  with members:
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "fpa cs focal plane array based compressive imaging in short wave infrared"
    - "quadratic basis pursuit"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "sparse recovery of streaming signals using l1 homotopy"
    - "lensless imaging by compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "guarantees of total variation minimization for signal recovery"):
  with members:
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "living on the edge phase transitions in convex programs with random data"
    - "stable recovery with analysis decomposable priors"
    - "sharp mse bounds for proximal denoising"
    - "the squared error of generalized lasso a precise analysis"
    - "analysis ell_1 recovery with frames and gaussian measurements"
    - "stable image reconstruction using total variation minimization"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"

Connected through hyperedge HE-4 (induced by source paper "chaotic analog to information conversion with chaotic state modulation"):
  with members:
    - "a compressed sensing framework of frequency sparse signals through chaotic system"
    - "compressed sensing with nonlinear observations and related nonlinear optimisation problems"
    - "segmented compressed sampling for analog to information conversion method and performance analysis"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-5 (induced by source paper "precisely verifying the null space conditions in compressed sensing a sandwiching algorithm"):
  with members:
    - "verifiable and computable performance analysis of sparsity recovery"
    - "fixed point theory and semidefinite programming for computable performance analysis of block sparsity recovery"

Connected through hyperedge HE-6 (induced by source paper "robust decentralized state estimation and tracking for power systems via network gossiping"):
  with members:
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-7 (induced by source paper "compressed sensing with nonlinear observations and related nonlinear optimisation problems"):
  with members:
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "model based compressive sensing"
    - "iterative hard thresholding for compressed sensing"

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)