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: "compressed sensing performance bounds under poisson noise"
  Abstract: This paper describes performance bounds for compressed sensing (CS) where the underlying sparse or compressible (sparsely approximable) signal is a vector of nonnegative intensities whose measurements are corrupted by Poisson noise. In this setting, standard CS techniques cannot be applied directly for several reasons. First, the usual signal-independent and/or bounded noise models do not apply to Poisson noise, which is nonadditive and signal-dependent. Second, the CS matrices typically considered are not feasible in real optical systems because they do not adhere to important constraints, such as nonnegativity and photon flux preservation. Third, the typical l2 - l1 minimization leads to overfitting in the high-intensity regions and oversmoothing in the low-intensity areas. In this paper, we describe how a feasible positivity- and flux-preserving sensing matrix can be constructed, and then analyze the performance of a CS reconstruction approach for Poisson data that minimizes an objective function consisting of a negative Poisson log likelihood term and a penalty term which measures signal sparsity. We show that, as the overall intensity of the underlying signal increases, an upper bound on the reconstruction error decays at an appropriate rate (depending on the compressibility of the signal), but that for a fixed signal intensity, the error bound actually grows with the number of measurements or sensors. This surprising fact is both proved theoretically and justified based on physical intuition.

Connected through hyperedge HE-1 (induced by source paper "estimation in poisson noise properties of the conditional mean estimator"):
  with members:
    - "mutual information relative entropy and estimation error in semi martingale channels"
    - "a de bruijn identity for discrete random variables"
    - "strong data processing inequalities for input constrained additive noise channels"
    - "bounds on the lambert function and their application to the outage analysis of user cooperation"
    - "on regret of parametric mismatch in minimum mean square error estimation"
    - "estimation in gaussian noise properties of the minimum mean square error"
    - "mutual information relative entropy and estimation in the poisson channel"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-2 (induced by source paper "sparse non negative recovery from biased subgaussian measurements using nnls"):
  with members:
    - "on the fly large scale channel gain estimation for massive antenna array base stations"
    - "visible light communications based indoor positioning via compressed sensing"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "learning without concentration"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"

Connected through hyperedge HE-3 (induced by source paper "reconstruction error bounds for compressed sensing under poisson or poisson gaussian noise using variance stabilization transforms"):
  with members:
    - "poisson matrix recovery and completion"
    - "the generalized lasso with non linear observations"
    - "learning exponential families in high dimensions strong convexity and sparsity"

Connected through hyperedge HE-4 (induced by source paper "poisson matrix recovery and completion"):
  with members:
    - "robust computation of linear models by convex relaxation"
    - "performance bounds for expander based compressed sensing in poisson noise"
    - "how well can we estimate a sparse vector"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "the power of convex relaxation near optimal matrix completion"
    - "set an algorithm for consistent matrix completion"
    - "matrix completion from noisy entries"
    - "matrix completion from a few entries"

Connected through hyperedge HE-5 (induced by source paper "robust estimation of self exciting point process models with application to neuronal modeling"):
  with members:
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-6 (induced by source paper "minimax lower bounds for noisy matrix completion under sparse factor models"):
  with members:
    - "matrix completion via max norm constrained optimization"
    - "recovering low rank matrices from few coefficients in any basis"
    - "low rank matrix recovery from errors and erasures"
    - "robust pca via outlier pursuit"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from noisy entries"
    - "matrix completion from a few entries"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-7 (induced by source paper "robust estimation of self exciting generalized linear models with application to neuronal modeling"):
  with members:
    - "sampling requirements for stable autoregressive estimation"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-8 (induced by source paper "compressive imaging and characterization of sparse light deflection maps"):
  with members:
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "compressive source separation theory and methods for hyperspectral imaging"
    - "analysis ell_1 recovery with frames and gaussian measurements"
    - "compressive optical deflectometric tomography a constrained total variation minimization approach"
    - "spread spectrum magnetic resonance imaging"
    - "theory and applications of compressed sensing"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"
    - "compressive schlieren deflectometry"

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)