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: "critical behavior and universality classes for an algorithmic phase transition in sparse reconstruction"
  Abstract: Optimization problems with sparsity-inducing penalties exhibit sharp algorithmic phase transitions when the numbers of variables tend to infinity, between regimes of `good' and `bad' performance. The nature of these phase transitions and associated universality classes remain incompletely understood. We analyze the mean field equations from the cavity method for two sparse reconstruction algorithms, Basis Pursuit and Elastic Net. These algorithms are associated with penalized regression problems $(\mathbf{y}-\mathbf{H} \mathbf{x})^2+\lambda V(\mathbf{x})$, where $V(\mathbf{x})$ is an algorithm-dependent penalty function. Here $\lambda$ is the penalty parameter, $\mathbf{x}$ is the $N$-dimensional, $K$-sparse solution to be recovered, $\mathbf{y}$ is the $M$ dimensional vector of measurements, and $\mathbf{H}$ is a known `random' matrix. In the limit $\lambda \rightarrow 0$ and $N\rightarrow \infty$, keeping $\rho=K/N$ fixed, exact recovery is possible for sufficiently large values of fractional measurement number $\alpha=M/N$, with an algorithmic phase transition occurring at a known critical value $\alpha_c = \alpha(\rho)$. We find that Basis Pursuit $V(\mathbf{x})=||\mathbf{x}||_{1} $and Elastic Net $V(\mathbf{x})=\lambda_1 ||\mathbf{x}||_{1} + \tfrac{\lambda_2}{2} ||\mathbf{x}||_{2}^2$ belong to two different universality classes. The Mean Squared Error goes to zero as $MSE \sim(\alpha_c-\alpha)^2$ as $\alpha$ approaches $\alpha_c$ from below for both algorithms. However, for Basis Pursuit, precisely on the phase transition line $\alpha=\alpha_c$, $MSE \sim \lambda^{4/3}$ for Basis Pursuit, whereas $MSE\sim\lambda$ for Elastic Net. We also find that in presence of additive noise, within the perfect reconstruction phase $\alpha>\alpha_c$ there is a non-zero setting for $\lambda$ that minimizes MSE, whereas at $\alpha=\alpha_c$ a finite $\lambda$ always increases the MSE.

Connected through hyperedge HE-1 (induced by source paper "understanding overfitting peaks in generalization error analytical risk curves for l 2 and l 1 penalized interpolation"):
  with members:
    - "understanding deep learning requires rethinking generalization"
    - "the cavity method for analysis of large scale penalized regression"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "message passing algorithms 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)