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: "structured sparsity and generalization"
  Abstract: We present a data dependent generalization bound for a large class of regularized algorithms which implement structured sparsity constraints. The bound can be applied to standard squared-norm regularization, the Lasso, the group Lasso, some versions of the group Lasso with overlapping groups, multiple kernel learning and other regularization schemes. In all these cases competitive results are obtained. A novel feature of our bound is that it can be applied in an infinite dimensional setting such as the Lasso in a separable Hilbert space or multiple kernel learning with a countable number of kernels.

Connected through hyperedge HE-1 (induced by source paper "efficient regularized piecewise linear regression trees"):
  with members:
    - "efficient non greedy optimization of decision trees"
    - "an inequality with applications to structured sparsity and multitask dictionary learning"

Connected through hyperedge HE-2 (induced by source paper "classification with sparse overlapping groups"):
  with members:
    - "sparse overlapping sets lasso for multitask learning and its application to fmri analysis"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "convex approaches to model wavelet sparsity patterns"
    - "collaborative hierarchical sparse modeling"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-3 (induced by source paper "conic multi task classification"):
  with members:
    - "pareto path multitask multiple kernel learning"
    - "a convex formulation for learning task relationships in multi task learning"
    - "convex multitask learning with flexible task clusters"
    - "regularization techniques for learning with matrices"

Connected through hyperedge HE-4 (induced by source paper "an inequality with applications to structured sparsity and multitask dictionary learning"):
  with members:
    - "sparse coding for multitask and transfer learning"
    - "regularization techniques for learning with matrices"
    - "a model of inductive bias learning"

Connected through hyperedge HE-5 (induced by source paper "on sparsity inducing regularization methods for machine learning"):
  with members:
    - "efficient first order methods for linear composite regularizers"

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)