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: "linear convergence of a frank wolfe type algorithm over trace norm balls"
  Abstract: We propose a rank-$k$ variant of the classical Frank-Wolfe algorithm to solve convex optimization over a trace-norm ball. Our algorithm replaces the top singular-vector computation ($1$-SVD) in Frank-Wolfe with a top-$k$ singular-vector computation ($k$-SVD), which can be done by repeatedly applying $1$-SVD $k$ times. Alternatively, our algorithm can be viewed as a rank-$k$ restricted version of projected gradient descent. We show that our algorithm has a linear convergence rate when the objective function is smooth and strongly convex, and the optimal solution has rank at most $k$. This improves the convergence rate and the total time complexity of the Frank-Wolfe method and its variants.

Connected through hyperedge HE-1 (induced by source paper "faster projection free online learning"):
  with members:
    - "introduction to online convex optimization"
    - "variance reduced and projection free stochastic optimization"
    - "a linearly convergent conditional gradient algorithm with applications to online and stochastic optimization"
    - "projection free online learning"

Connected through hyperedge HE-2 (induced by source paper "communication efficient asynchronous stochastic frank wolfe over nuclear norm balls"):
  with members:
    - "quantized frank wolfe communication efficient distributed optimization"
    - "quantized frank wolfe faster optimization lower communication and projection free"
    - "a distributed frank wolfe framework for learning low rank matrices with the trace norm"
    - "algorithmic regularization in over parameterized matrix recovery"
    - "algorithmic regularization in over parameterized matrix sensing and neural networks with quadratic activations"
    - "variance reduced and projection free stochastic optimization"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"

Connected through hyperedge HE-3 (induced by source paper "improved regret bounds for projection free bandit convex optimization"):
  with members:
    - "introduction to online convex optimization"
    - "kernel based methods for bandit convex optimization"
    - "an optimal algorithm for bandit convex optimization"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "a hybrid algorithm for convex semidefinite optimization"
    - "block coordinate frank wolfe optimization for structural svms"
    - "projection free online learning"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-4 (induced by source paper "primal dual block frank wolfe"):
  with members:
    - "variance reduced and projection free stochastic optimization"
    - "block coordinate frank wolfe optimization for structural svms"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-5 (induced by source paper "neural conditional gradients"):
  with members:
    - "learning to optimize neural nets"
    - "a distributed frank wolfe framework for learning low rank matrices with the trace norm"
    - "optnet differentiable optimization as a layer in neural networks"
    - "learning to reinforcement learn"
    - "learning to learn by gradient descent by gradient descent"
    - "matching networks for one shot learning"
    - "adam a method for stochastic optimization"
    - "deep learning using linear support vector machines"

Connected through hyperedge HE-6 (induced by source paper "online compact convexified factorization machine"):
  with members:
    - "introduction to online convex optimization"
    - "field aware factorization machines in a real world online advertising system"
    - "incremental factorization machines for persistently cold starting online item recommendation"
    - "projection free online learning"

Connected through hyperedge HE-7 (induced by source paper "fast generalized conditional gradient method with applications to matrix recovery problems"):
  with members:
    - "fast algorithms for robust pca via gradient descent"
    - "robust principal component analysis"

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)