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: "safe feature elimination for the lasso and sparse supervised learning problems"
  Abstract: We describe a fast method to eliminate features (variables) in l1 -penalized least-square regression (or LASSO) problems. The elimination of features leads to a potentially substantial reduction in running time, specially for large values of the penalty parameter. Our method is not heuristic: it only eliminates features that are guaranteed to be absent after solving the LASSO problem. The feature elimination step is easy to parallelize and can test each feature for elimination independently. Moreover, the computational effort of our method is negligible compared to that of solving the LASSO problem - roughly it is the same as single gradient step. Our method extends the scope of existing LASSO algorithms to treat larger data sets, previously out of their reach. We show how our method can be extended to general l1 -penalized convex problems and present preliminary results for the Sparse Support Vector Machine and Logistic Regression problems.

Connected through hyperedge HE-1 (induced by source paper "safe screening for the generalized conditional gradient method"):
  with members:
    - "deep frank wolfe for neural network optimization"
    - "multi task learning as multi objective optimization"
    - "joint screening tests for lasso"
    - "tight bounds for approximate carath eodory and beyond"
    - "the ordered weighted ell_1 norm atomic formulation projections and algorithms"
    - "a safe screening rule for sparse logistic regression"
    - "safe screening with variational inequalities and its application to lasso"
    - "duality between subgradient and conditional gradient methods"

Connected through hyperedge HE-2 (induced by source paper "tighter bound estimation of sensitivity analysis for incremental and decremental data modification"):
  with members:
    - "screening tests for lasso problems"
    - "a safe screening rule for sparse logistic regression"
    - "safe screening with variational inequalities and its application to lasso"

Connected through hyperedge HE-3 (induced by source paper "learning high order feature interactions with fine control kernels"):
  with members:
    - "moleculenet a benchmark for molecular machine learning"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-4 (induced by source paper "safe sample screening for robust support vector machine"):
  with members:
    - "screening rules for lasso with non convex sparse regularizers"
    - "scaling svm and least absolute deviations via exact data reduction"
    - "a safe screening rule for sparse logistic regression"
    - "safe screening with variational inequalities and its application to lasso"

Connected through hyperedge HE-5 (induced by source paper "screening data points in empirical risk minimization via ellipsoidal regions and safe loss functions"):
  with members:
    - "a safe screening rule for sparse logistic regression"

Connected through hyperedge HE-6 (induced by source paper "sparse regression and adaptive feature generation for the discovery of dynamical systems"):
  with members:
    - "mini batch semi stochastic gradient descent in the proximal setting"

Connected through hyperedge HE-7 (induced by source paper "screening tests for lasso problems"):
  with members:
    - "feedback controlled sequential lasso screening"
    - "safe screening with variational inequalities and its application to lasso"
    - "lasso screening rules via dual polytope projection"
    - "safe feature elimination in sparse supervised learning"
    - "fast l1 minimization algorithms for robust face recognition"

Connected through hyperedge HE-8 (induced by source paper "distributed convolutional sparse coding"):
  with members:
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "scaling up coordinate descent algorithms for large ell_1 regularization problems"
    - "parallel coordinate descent for l1 regularized loss minimization"

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)