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: "an exponential lower bound on the complexity of regularization paths"
  Abstract: For a variety of regularized optimization problems in machine learning, algorithms computing the entire solution path have been developed recently. Most of these methods are quadratic programs that are parameterized by a single parameter, as for example the Support Vector Machine (SVM). Solution path algorithms do not only compute the solution for one particular value of the regularization parameter but the entire path of solutions, making the selection of an optimal parameter much easier. #R##N#It has been assumed that these piecewise linear solution paths have only linear complexity, i.e. linearly many bends. We prove that for the support vector machine this complexity can be exponential in the number of training points in the worst case. More strongly, we construct a single instance of n input points in d dimensions for an SVM such that at least \Theta(2^{n/2}) = \Theta(2^d) many distinct subsets of support vectors occur as the regularization parameter changes.

Connected through hyperedge HE-1 (induced by source paper "minding the gaps for block frank wolfe optimization of structured svms"):
  with members:
    - "a multi plane block coordinate frank wolfe algorithm for training structural svms with a costly max oracle"
    - "articulated pose estimation by a graphical model with image dependent pairwise relations"
    - "reflection methods for user friendly submodular optimization"
    - "a novel frank wolfe algorithm analysis and applications to large scale svm training"
    - "block coordinate frank wolfe optimization for structural svms"
    - "suboptimal solution path algorithm for support vector machine"
    - "introduction to the conll 2000 shared task chunking"

Connected through hyperedge HE-2 (induced by source paper "an equivalence between the lasso and support vector machines"):
  with members:
    - "linear regression with limited observation"
    - "sublinear time measurement optimal sparse recovery for all"
    - "efficient learning with partially observed attributes"
    - "safe feature elimination for the lasso and sparse supervised learning problems"
    - "sublinear optimization for machine learning"

Connected through hyperedge HE-3 (induced by source paper "suboptimal solution path algorithm for support vector machine"):
  with members:
    - "a combinatorial algorithm to compute regularization paths"

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)