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: "a modular analysis of adaptive non convex optimization optimism composite objectives and variational bounds"
  Abstract: Recently, much work has been done on extending the scope of online learning and incremental stochastic optimization algorithms. In this paper we contribute to this effort in two ways: First, based on a new regret decomposition and a generalization of Bregman divergences, we provide a self-contained, modular analysis of the two workhorses of online learning: (general) adaptive versions of Mirror Descent (MD) and the Follow-the-Regularized-Leader (FTRL) algorithms. The analysis is done with extra care so as not to introduce assumptions not needed in the proofs and allows to combine, in a straightforward way, different algorithmic ideas (e.g., adaptivity, optimism, implicit updates) and learning settings (e.g., strongly convex or composite objectives). This way we are able to reprove, extend and refine a large body of the literature, while keeping the proofs concise. The second contribution is a byproduct of this careful analysis: We present algorithms with improved variational bounds for smooth, composite objectives, including a new family of optimistic MD algorithms with only one projection step per round. Furthermore, we provide a simple extension of adaptive regret bounds to practically relevant non-convex problem settings with essentially no extra effort.

Connected through hyperedge HE-1 (induced by source paper "provably efficient adaptive approximate policy iteration"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "worst case regret bounds for exploration via randomized value functions"
    - "provably efficient exploration in policy optimization"
    - "a theory of regularized markov decision processes"
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "is q learning provably efficient"

Connected through hyperedge HE-2 (induced by source paper "a modern introduction to online learning"):
  with members:
    - "on the convergence of adam and beyond"
    - "black box reductions for parameter free online learning in banach spaces"
    - "training deep networks without learning rates through coin betting"
    - "scale free online learning"
    - "coin betting and parameter free online learning"
    - "fighting bandits with a new kind of smoothness"
    - "second order quantile methods for experts and combinatorial games"
    - "prediction with advice of unknown number of experts"

Connected through hyperedge HE-3 (induced by source paper "bandit principal component analysis"):
  with members:
    - "bilinear bandits with low rank structure"
    - "introduction to online convex optimization"
    - "more adaptive algorithms for adversarial bandits"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "sparsity variance and curvature in multi armed bandits"
    - "stochastic low rank bandits"
    - "corralling a band of bandit algorithms"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"

Connected through hyperedge HE-4 (induced by source paper "on first order bounds variance and gap dependent bounds for adversarial bandits"):
  with members:
    - "first order regret analysis of thompson sampling"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "first order regret bounds for combinatorial semi bandits"

Connected through hyperedge HE-5 (induced by source paper "dynamic online gradient descent with improved query complexity a theoretical revisit"):
  with members:
    - "introduction to online convex optimization"
    - "fast rates for online gradient descent without strong convexity via hoffman s bound"
    - "convergence of unregularized online learning algorithms"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "tracking slowly moving clairvoyant optimal dynamic regret of online learning with true and noisy gradient"
    - "streaming recommender systems"
    - "online optimization competing with dynamic comparators"
    - "online gradient descent in function space"

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)