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: "model selection through sparse maximum likelihood estimation"
  Abstract: We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added l_1-norm penalty term. The problem as formulated is convex but the memory requirements and complexity of existing interior point methods are prohibitive for problems with more than tens of nodes. We present two new algorithms for solving problems with at least a thousand nodes in the Gaussian case. Our first algorithm uses block coordinate descent, and can be interpreted as recursive l_1-norm penalized regression. Our second algorithm, based on Nesterov's first order method, yields a complexity estimate with a better dependence on problem size than existing interior point methods. Using a log determinant relaxation of the log partition function (Wainwright & Jordan (2006)), we show that these same algorithms can be used to solve an approximate sparse maximum likelihood problem for the binary case. We test our algorithms on synthetic data, as well as on gene expression and senate voting records data.

Connected through hyperedge HE-1 (induced by source paper "the quality of the covariance selection through detection problem and auc bounds"):
  with members:
    - "latent tree approximation in linear model"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "on pairs of f divergences and their joint range"

Connected through hyperedge HE-2 (induced by source paper "the goodness of covariance selection problem from auc bounds"):
  with members:
    - "the quality of the covariance selection through detection problem and auc bounds"

Connected through hyperedge HE-3 (induced by source paper "estimation of simultaneously sparse and low rank matrices"):
  with members:
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "social structure of facebook networks"
    - "robust principal component analysis"
    - "non negative matrix factorization with sparseness constraints"
    - "a direct formulation for sparse pca using semidefinite programming"

Connected through hyperedge HE-4 (induced by source paper "sparse approximation via penalty decomposition methods"):
  with members:
    - "sparse inverse covariance matrix estimation using quadratic approximation"
    - "ell_0 minimization for wavelet frame based image restoration"
    - "iterative hard thresholding for compressed sensing"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-5 (induced by source paper "self concordant analysis for logistic regression"):
  with members:
    - "bolasso model consistent lasso estimation through the bootstrap"
    - "consistency of the group lasso and multiple kernel learning"

Connected through hyperedge HE-6 (induced by source paper "identifying small mean reverting portfolios"):
  with members:
    - "a direct formulation for sparse pca using semidefinite programming"

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.AI(Artificial Intelligence)