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: "simple bounds for noisy linear inverse problems with exact side information"
  Abstract: This paper considers the linear inverse problem where we wish to estimate a structured signal x_0 from its corrupted observations. When the problem is ill-posed, it is natural to associate a convex function f(·) with the structure of the signal. For example, l_1 norm can be used for sparse signals. To carry out the estimation, we consider two well-known convex programs: 1) Second order cone program (SOCP), and, 2) Lasso. Assuming Gaussian measurements, we show that, if precise information about the value f(x_0)#R##N#or the l_2-norm of the noise is available, one can do a particularly good job at estimation. In particular, the#R##N#reconstruction error becomes proportional to the “sparsity” of the signal rather than to the ambient dimension of the noise vector. We connect our results to the existing literature and provide a discussion on their relation to the standard least-squares problem. Our error bounds are non-asymptotic and sharp, they apply to arbitrary convex functions and do not assume any distribution#R##N#on the noise.

Connected through hyperedge HE-1 (induced by source paper "sharp time data tradeoffs for linear inverse problems"):
  with members:
    - "isometric sketching of any set via the restricted isometry property"
    - "the generalized lasso with non linear observations"
    - "does ell_p minimization outperform ell_1 minimization"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "learning without concentration"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "from denoising to compressed sensing"
    - "living on the edge phase transitions in convex programs with random data"

Connected through hyperedge HE-2 (induced by source paper "the generalized lasso with non linear observations"):
  with members:
    - "convex recovery of a structured signal from independent random linear measurements"
    - "learning without concentration"
    - "on the effective measure of dimension in the analysis cosparse model"
    - "living on the edge phase transitions in convex programs with random data"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "the squared error of generalized lasso a precise analysis"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing with non gaussian measurements"

Connected through hyperedge HE-3 (induced by source paper "convex recovery of a structured signal from independent random linear measurements"):
  with members:
    - "learning without concentration"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "living on the edge phase transitions in convex programs with random data"
    - "sharp mse bounds for proximal denoising"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "recovering low rank matrices from few coefficients in any basis"

Connected through hyperedge HE-4 (induced by source paper "the gaussian min max theorem in the presence of convexity"):
  with members:
    - "convex recovery of a structured signal from independent random linear measurements"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "from denoising to compressed sensing"
    - "living on the edge phase transitions in convex programs with random data"
    - "a framework to characterize performance of lasso algorithms"
    - "the squared error of generalized lasso a precise analysis"
    - "upper bounding ell_1 optimization weak thresholds"
    - "meshes that trap random subspaces"

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)