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: "duality between subgradient and conditional gradient methods"
  Abstract: Given a convex optimization problem and its dual, there are many possible first-order algorithms. In this paper, we show the equivalence between mirror descent algorithms and algorithms generalizing the conditional gradient method. This is done through convex duality, and implies notably that for certain problems, such as for supervised machine learning problems with non-smooth losses or problems regularized by non-smooth regularizers, the primal subgradient method and the dual conditional gradient method are formally equivalent. The dual interpretation leads to a form of line search for mirror descent, as well as guarantees of convergence for primal-dual certificates.

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"
    - "block coordinate frank wolfe optimization for structural svms"

Connected through hyperedge HE-2 (induced by source paper "a distributed quasi newton algorithm for primal and dual regularized empirical risk minimization"):
  with members:
    - "a distributed second order algorithm you can trust"
    - "using neural networks to detect line outages from pmu data"
    - "proximal quasi newton methods for regularized convex optimization with linear and accelerated sublinear convergence rates"
    - "a general distributed dual coordinate optimization framework for regularized loss minimization"
    - "mllib machine learning in apache spark"
    - "practical inexact proximal quasi newton method with global complexity analysis"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-3 (induced by source paper "a refined primal dual analysis of the implicit bias"):
  with members:
    - "gradient descent aligns the layers of deep linear networks"
    - "risk and parameter convergence of logistic regression"
    - "spectrally normalized margin bounds for neural networks"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-4 (induced by source paper "deep frank wolfe for neural network optimization"):
  with members:
    - "on the convergence of adam and beyond"
    - "smooth loss functions for deep top k classification"
    - "faster convergence generalization in dnns"
    - "online learning rate adaptation with hypergradient descent"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "exploring generalization in deep learning"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "learned optimizers that scale and generalize"

Connected through hyperedge HE-5 (induced by source paper "efficient linear programming for dense crfs"):
  with members:
    - "efficient continuous relaxations for dense crf"
    - "minding the gaps for block frank wolfe optimization of structured svms"
    - "efficient sdp inference for fully connected crfs based on low rank decomposition"
    - "conditional random fields as recurrent neural networks"
    - "fully connected deep structured networks"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "a multi plane block coordinate frank wolfe algorithm for training structural svms with a costly max oracle"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-6 (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"
    - "an equivalence between the lasso and support vector machines"

Connected through hyperedge HE-7 (induced by source paper "submodular functions from discrete to continous domains"):
  with members:
    - "scalable variational inference in log supermodular models"
    - "reflection methods for user friendly submodular optimization"
    - "learning with submodular functions a convex optimization perspective"
    - "minimizing a sum of submodular functions"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "breaking the curse of dimensionality with convex neural networks"):
  with members:
    - "on the equivalence between quadrature rules and random features"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "on the computational efficiency of training neural networks"
    - "computational aspects of the hausdorff distance in unbounded dimension"
    - "convex relaxations of structured matrix factorizations"
    - "learning from an optimization viewpoint"
    - "exploring large feature spaces with hierarchical multiple kernel learning"
    - "consistency of the group lasso and 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)