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: "the squared error of generalized lasso a precise analysis"
  Abstract: We consider the problem of estimating an unknown signal $x_0$ from noisy linear observations $y = Ax_0 + z\in R^m$. In many practical instances, $x_0$ has a certain structure that can be captured by a structure inducing convex function $f(\cdot)$. For example, $\ell_1$ norm can be used to encourage a sparse solution. To estimate $x_0$ with the aid of $f(\cdot)$, we consider the well-known LASSO method and provide sharp characterization of its performance. We assume the entries of the measurement matrix $A$ and the noise vector $z$ have zero-mean normal distributions with variances $1$ and $\sigma^2$ respectively. For the LASSO estimator $x^*$, we attempt to calculate the Normalized Square Error (NSE) defined as $\frac{\|x^*-x_0\|_2^2}{\sigma^2}$ as a function of the noise level $\sigma$, the number of observations $m$ and the structure of the signal. We show that, the structure of the signal $x_0$ and choice of the function $f(\cdot)$ enter the error formulae through the summary parameters $D(cone)$ and $D(\lambda)$, which are defined as the Gaussian squared-distances to the subdifferential cone and to the $\lambda$-scaled subdifferential, respectively. The first LASSO estimator assumes a-priori knowledge of $f(x_0)$ and is given by $\arg\min_{x}\{{\|y-Ax\|_2}~\text{subject to}~f(x)\leq f(x_0)\}$. We prove that its worst case NSE is achieved when $\sigma\rightarrow 0$ and concentrates around $\frac{D(cone)}{m-D(cone)}$. Secondly, we consider $\arg\min_{x}\{\|y-Ax\|_2+\lambda f(x)\}$, for some $\lambda\geq 0$. This time the NSE formula depends on the choice of $\lambda$ and is given by $\frac{D(\lambda)}{m-D(\lambda)}$. We then establish a mapping between this and the third estimator $\arg\min_{x}\{\frac{1}{2}\|y-Ax\|_2^2+ \lambda f(x)\}$. Finally, for a number of important structured signal classes, we translate our abstract formulae to closed-form upper bounds on the NSE.

Connected through hyperedge HE-1 (induced by source paper "the generalized lasso for sub gaussian measurements with dithered quantization"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "learning compact neural networks with regularization"
    - "non gaussian hyperplane tessellations and robust one bit compressed sensing"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "learning without concentration"
    - "living on the edge phase transitions in convex programs with random data"
    - "a framework to characterize performance of lasso algorithms"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-2 (induced by source paper "sensitivity of ell_ 1 minimization to parameter choice"):
  with members:
    - "the generalized lasso with non linear observations"
    - "living on the edge phase transitions in convex programs with random data"
    - "sharp mse bounds for proximal denoising"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "how well can we estimate a sparse vector"

Connected through hyperedge HE-3 (induced by source paper "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"):
  with members:
    - "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "sparse phase retrieval via truncated amplitude flow"
    - "non convex phase retrieval from stft measurements"
    - "reconstruction of signals from their autocorrelation and cross correlation vectors with applications to phase retrieval and blind channel estimation"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "compressed sensing from phaseless gaussian measurements via linear programming in the natural parameter space"
    - "phasemax convex phase retrieval via basis pursuit"

Connected through hyperedge HE-4 (induced by source paper "precise error analysis of regularized m estimators in high dimensions"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "ber analysis of the box relaxation for bpsk signal recovery"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"
    - "the generalized lasso with non linear observations"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "statistical estimation from denoising to sparse regression and hidden cliques"
    - "convexity in source separation models geometry and algorithms"

Connected through hyperedge HE-5 (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"
    - "simple bounds for noisy linear inverse problems with exact side information"

Connected through hyperedge HE-6 (induced by source paper "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "a framework to characterize performance of lasso algorithms"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-7 (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"
    - "simple bounds for noisy linear inverse problems with exact side information"
    - "living on the edge phase transitions in convex programs with random data"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "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-8 (induced by source paper "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "a tight version of the gaussian min max theorem in the presence of convexity"
    - "on sparse vector recovery performance in structurally orthogonal matrices via lasso"
    - "the gaussian min max theorem in the presence of convexity"
    - "living on the edge phase transitions in convex programs with random data"
    - "a framework to characterize performance of lasso algorithms"
    - "optimal phase transitions in 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)