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: "shaping level sets with submodular functions"
  Abstract: We consider a class of sparsity-inducing regularization terms based on submodular functions. While previous work has focused on non-decreasing functions, we explore symmetric submodular functions and their \lova extensions. We show that the Lovasz extension may be seen as the convex envelope of a function that depends on level sets (i.e., the set of indices whose corresponding components of the underlying predictor are greater than a given constant): this leads to a class of convex structured regularization terms that impose prior knowledge on the level sets, and not only on the supports of the underlying predictors. We provide a unified set of optimization algorithms, such as proximal operators, and theoretical guarantees (allowed level sets and recovery conditions). By selecting specific submodular functions, we give a new interpretation to known norms, such as the total variation; we also define new norms, in particular ones that are based on order statistics with application to clustering and outlier detection, and on noisy cuts in graphs with application to change point detection in the presence of outliers.

Connected through hyperedge HE-1 (induced by source paper "fast decomposable submodular function minimization using constrained total variation"):
  with members:
    - "decomposable submodular function minimization discrete and continuous"
    - "random coordinate descent methods for minimizing decomposable submodular functions"
    - "submodular functions from discrete to continous domains"
    - "convex optimization for parallel energy minimization"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "provable submodular minimization using wolfe s algorithm"
    - "reflection methods for user friendly submodular optimization"
    - "a distributed mincut maxflow algorithm combining path augmentation and push relabel"

Connected through hyperedge HE-2 (induced by source paper "parametric maxflows for structured sparse learning with convex relaxations of submodular functions"):
  with members:
    - "sparse modeling for image and vision processing"
    - "structured convex optimization under submodular constraints"
    - "structured sparsity through convex optimization"
    - "learning with submodular functions a convex optimization perspective"
    - "structured sparsity inducing norms through submodular functions"

Connected through hyperedge HE-3 (induced by source paper "learning with submodular functions a convex optimization perspective"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "maximizing submodular functions using probabilistic graphical models"
    - "reflection methods for user friendly submodular optimization"
    - "convex relaxations of structured matrix factorizations"
    - "duality between subgradient and conditional gradient methods"
    - "a submodular supermodular procedure with applications to discriminative structure learning"
    - "pac learning bounded tree width graphical models"
    - "near optimal nonmyopic value of information in graphical models"

Connected through hyperedge HE-4 (induced by source paper "structured sparsity through convex optimization"):
  with members:
    - "smoothing proximal gradient method for general structured sparse learning"
    - "near optimal nonmyopic value of information in graphical models"
    - "optimization with sparsity inducing penalties"
    - "convex approaches to model wavelet sparsity patterns"
    - "convergence rates of inexact proximal gradient methods for convex optimization"
    - "learning with submodular functions a convex optimization perspective"
    - "network flow algorithms for structured sparsity"
    - "structured sparsity inducing norms through submodular functions"

Connected through hyperedge HE-5 (induced by source paper "optimization with sparsity inducing penalties"):
  with members:
    - "convex approaches to model wavelet sparsity patterns"
    - "convergence rates of inexact proximal gradient methods for convex optimization"
    - "robust pca via outlier pursuit"
    - "network flow algorithms for structured sparsity"
    - "structured sparsity inducing norms through submodular functions"
    - "model based compressive sensing"
    - "collaborative hierarchical sparse modeling"
    - "convex sparse matrix factorizations"

Connected through hyperedge HE-6 (induced by source paper "structured sparsity inducing norms through submodular functions"):
  with members:
    - "near optimal nonmyopic value of information in graphical models"
    - "network flow algorithms for structured sparsity"
    - "model based compressive sensing"
    - "convex analysis and optimization with submodular functions a tutorial"
    - "exploring large feature spaces with hierarchical multiple kernel learning"

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)