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 equivalence between the lasso and support vector machines"
  Abstract: We investigate the relation of two fundamental tools in machine learning and signal processing, that is the support vector machine (SVM) for classification, and the Lasso technique used in regression. We show that the resulting optimization problems are equivalent, in the following sense. Given any instance of an $\ell_2$-loss soft-margin (or hard-margin) SVM, we construct a Lasso instance having the same optimal solutions, and vice versa. #R##N#As a consequence, many existing optimization algorithms for both SVMs and Lasso can also be applied to the respective other problem instances. Also, the equivalence allows for many known theoretical insights for SVM and Lasso to be translated between the two settings. One such implication gives a simple kernelized version of the Lasso, analogous to the kernels used in the SVM setting. Another consequence is that the sparsity of a Lasso solution is equal to the number of support vectors for the corresponding SVM instance, and that one can use screening rules to prune the set of support vectors. Furthermore, we can relate sublinear time algorithms for the two problems, and give a new such algorithm variant for the Lasso. We also study the regularization paths for both methods.

Connected through hyperedge HE-1 (induced by source paper "data encoding for byzantine resilient distributed optimization"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "stochastic distributed and federated optimization for machine learning"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "modified frank wolfe algorithm for enhanced sparsity in support vector machine classifiers"):
  with members:
    - "training support vector machines using frank wolfe optimization methods"

Connected through hyperedge HE-3 (induced by source paper "primal dual rates and certificates"):
  with members:
    - "adding vs averaging in distributed primal dual optimization"
    - "linear convergence of the randomized feasible descent method under the weak strong convexity assumption"
    - "distributed optimization with arbitrary local solvers"
    - "sdca without duality"
    - "distributed mini batch sdca"
    - "l1 regularized distributed optimization a communication efficient primal dual framework"
    - "mini batch primal and dual methods for svms"
    - "duality between subgradient and conditional gradient methods"

Connected through hyperedge HE-4 (induced by source paper "l1 regularized distributed optimization a communication efficient primal dual framework"):
  with members:
    - "a general distributed dual coordinate optimization framework for regularized loss minimization"
    - "adding vs averaging in distributed primal dual optimization"
    - "mllib machine learning in apache spark"
    - "parallel coordinate descent for l1 regularized loss minimization"

Connected through hyperedge HE-5 (induced by source paper "equivalence of learning algorithms"):
  with members:
    - "stability of multi task kernel regression algorithms"
    - "robustness and generalization"

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)