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: "variance reduced and projection free stochastic optimization"
  Abstract: The Frank-Wolfe optimization algorithm has recently regained popularity for machine learning applications due to its projection-free property and its ability to handle structured constraints. However, in the stochastic learning setting, it is still relatively understudied compared to the gradient descent counterpart. In this work, leveraging a recent variance reduction technique, we propose two stochastic Frank-Wolfe variants which substantially improve previous results in terms of the number of stochastic gradient evaluations needed to achieve $1-\epsilon$ accuracy. For example, we improve from $O(\frac{1}{\epsilon})$ to $O(\ln\frac{1}{\epsilon})$ if the objective function is smooth and strongly convex, and from $O(\frac{1}{\epsilon^2})$ to $O(\frac{1}{\epsilon^{1.5}})$ if the objective function is smooth and Lipschitz. The theoretical improvement is also observed in experiments on real-world datasets for a multiclass classification application.

Connected through hyperedge HE-1 (induced by source paper "on the convergence of stochastic gradient descent with low rank projections for convex low rank matrix problems"):
  with members:
    - "fast stochastic algorithms for low rank and nonsmooth matrix problems"
    - "fast algorithms for robust pca via gradient descent"
    - "matrix completion has no spurious local minimum"
    - "provable efficient online matrix completion via non convex stochastic gradient descent"
    - "projection free online learning"
    - "phase retrieval via matrix completion"
    - "making gradient descent optimal for strongly convex stochastic optimization"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-2 (induced by source paper "faster projection free online learning"):
  with members:
    - "introduction to online convex optimization"
    - "linear convergence of a frank wolfe type algorithm over trace norm balls"
    - "a linearly convergent conditional gradient algorithm with applications to online and stochastic optimization"
    - "projection free online learning"

Connected through hyperedge HE-3 (induced by source paper "black box submodular maximization discrete and continuous settings"):
  with members:
    - "introduction to online convex optimization"
    - "online continuous submodular maximization from full information to bandit feedback"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "black box adversarial attacks with limited queries and information"
    - "discrete adversarial attacks and submodular optimization with applications to text classification"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "gradient methods for submodular maximization"
    - "continuous dr submodular maximization structure and algorithms"

Connected through hyperedge HE-4 (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"
    - "linear convergence of a frank wolfe type algorithm over trace norm balls"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"

Connected through hyperedge HE-5 (induced by source paper "quantized frank wolfe faster optimization lower communication and projection free"):
  with members:
    - "constrained deep learning using conditional gradient and applications in computer vision"
    - "deep frank wolfe for neural network optimization"
    - "a distributed frank wolfe framework for learning low rank matrices with the trace norm"
    - "neural conditional gradients"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "learning infinite rbms with frank wolfe"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"

Connected through hyperedge HE-6 (induced by source paper "efficient projection free online methods with stochastic recursive gradient"):
  with members:
    - "introduction to online convex optimization"
    - "momentum based variance reduction in non convex sgd"
    - "quantized frank wolfe faster optimization lower communication and projection free"
    - "online learning a comprehensive survey"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "block coordinate frank wolfe optimization for structural svms"
    - "projection free online learning"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-7 (induced by source paper "an widetilde mathcal o m varepsilon 3 5 cost algorithm for semidefinite programs with diagonal constraints"):
  with members:
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "a direct formulation for sparse pca using semidefinite programming"

Connected through hyperedge HE-8 (induced by source paper "apprenticeship learning via frank wolfe"):
  with members:
    - "average reward reinforcement learning with unknown mixing times"
    - "a linearly convergent conditional gradient algorithm with applications to online and stochastic optimization"
    - "projection free online learning"
    - "strategy iteration is strongly polynomial for 2 player turn based stochastic games with a constant discount factor"

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)