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: "learning structural changes of gaussian graphical models in controlled experiments"
  Abstract: Graphical models are widely used in scienti fic and engineering research to represent conditional independence structures between random variables. In many controlled experiments, environmental changes or external stimuli can often alter the conditional dependence between the random variables, and potentially produce significant structural changes in the corresponding graphical models. Therefore, it is of great importance to be able to detect such structural changes from data, so as to gain novel insights into where and how the structural changes take place and help the system adapt to the new environment. Here we report an effective learning strategy to extract structural changes in Gaussian graphical model using l1-regularization based convex optimization. We discuss the properties of the problem formulation and introduce an efficient implementation by the block coordinate descent algorithm. We demonstrate the principle of the approach on a numerical simulation experiment, and we then apply the algorithm to the modeling of gene regulatory networks under different conditions and obtain promising yet biologically plausible results.

Connected through hyperedge HE-1 (induced by source paper "regularized loss minimizers with local data perturbation consistency and data irrecoverability"):
  with members:
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "calibrating data to sensitivity in private data analysis"
    - "estimation of simultaneously sparse and low rank matrices"
    - "network flow algorithms for structured sparsity"
    - "information theoretic limits of selecting binary graphical models in high dimensions"
    - "differentially private empirical risk minimization"
    - "learning exponential families in high dimensions strong convexity and sparsity"

Connected through hyperedge HE-2 (induced by source paper "lower bounds for two sample structural change detection in ising and gaussian models"):
  with members:
    - "testing ising models"
    - "efficiently learning ising models on arbitrary graphs"
    - "on the information theoretic limits of learning ising models"
    - "high dimensional gaussian graphical model selection walk summability and local separation criterion"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-3 (induced by source paper "a constrained l1 minimization approach for estimating multiple sparse gaussian or nonparanormal graphical models"):
  with members:
    - "sparse inverse covariance matrix estimation using quadratic approximation"
    - "density difference estimation"

Connected through hyperedge HE-4 (induced by source paper "simultaneous and group sparse multi task learning of gaussian graphical models"):
  with members:
    - "projected subgradient methods for learning sparse gaussians"
    - "high dimensional sparse inverse covariance estimation using greedy methods"
    - "sparse inverse covariance selection via alternating linearization methods"

Connected through hyperedge HE-5 (induced by source paper "lipschitz parametrization of probabilistic graphical models"):
  with members:
    - "convex structure learning for bayesian networks polynomial feature selection and approximate ordering"

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.LG(Machine Learning)