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 generalized online mirror descent with applications to classification and regression"
  Abstract: Online learning algorithms are fast, memory-efficient, easy to implement, and applicable to many prediction problems, including classification, regression, and ranking. Several online algorithms were proposed in the past few decades, some based on additive updates, like the Perceptron, and some on multiplicative updates, like Winnow. A unified viewpoint on the design and the analysis of online algorithms is provided by online mirror descent, a general prediction strategy from which most first-order algorithms can be obtained as special cases. #R##N#We generalize online mirror descent to sequences of time-varying regularizers with generic updates. Unlike standard mirror descent, our more general formulation also captures second order algorithms, algorithms for composite losses, and algorithms for adaptive filtering. Moreover, we recover, and sometimes improve, known regret bounds by instantiating our analysis on specific regularizers. Finally, we show the power of our approach by deriving a new second order algorithm with a regret bound invariant with respect to arbitrary rescalings of individual features.

Connected through hyperedge HE-1 (induced by source paper "logarithmic regret for adversarial online control"):
  with members:
    - "logarithmic regret for learning linear quadratic regulators efficiently"
    - "improper learning for non stochastic control"
    - "naive exploration is optimal for online lqr"
    - "online convex optimization in adversarial markov decision processes"
    - "introduction to online convex optimization"
    - "online control with adversarial disturbances"
    - "logarithmic regret for online control"
    - "the nonstochastic control problem"

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"
    - "a modular analysis of adaptive non convex optimization optimism composite objectives and variational bounds"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "adaptive scale invariant online algorithms for learning linear models"):
  with members:
    - "introduction to online convex optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "training deep networks without learning rates through coin betting"
    - "online learning without prior information"
    - "coin betting and parameter free online learning"
    - "scale free algorithms for online linear optimization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "cooperative online learning keeping your neighbors updated"):
  with members:
    - "introduction to online convex optimization"
    - "decentralized cooperative stochastic multi armed bandits"
    - "delay and cooperation in nonstochastic bandits"
    - "online learning with feedback graphs beyond bandits"
    - "from bandits to experts on the value of side observations"

Connected through hyperedge HE-5 (induced by source paper "tsallis inf an optimal algorithm for stochastic and adversarial bandits"):
  with members:
    - "connections between mirror descent thompson sampling and the information ratio"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "stochastic bandits robust to adversarial corruptions"
    - "more adaptive algorithms for adversarial bandits"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "corralling a band of bandit algorithms"
    - "an algorithm with nearly optimal pseudo regret for both stochastic and adversarial bandits"
    - "fighting bandits with a new kind of smoothness"

Connected through hyperedge HE-6 (induced by source paper "training deep networks without learning rates through coin betting"):
  with members:
    - "online convex optimization with unconstrained domains and losses"
    - "online learning without prior information"
    - "coin betting and parameter free online learning"
    - "gradient descent learns linear dynamical systems"
    - "scale free algorithms for online linear optimization"
    - "adam a method for stochastic optimization"
    - "caffe convolutional architecture for fast feature embedding"
    - "simultaneous model selection and optimization through parameter free stochastic learning"

Connected through hyperedge HE-7 (induced by source paper "scale invariant unconstrained online learning"):
  with members:
    - "introduction to online convex optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "online convex optimization with unconstrained domains and losses"
    - "training deep networks without learning rates through coin betting"
    - "online learning without prior information"
    - "efficient second order online learning by sketching"
    - "coin betting and parameter free online learning"
    - "scale free algorithms for online linear optimization"

Connected through hyperedge HE-8 (induced by source paper "efficient online bandit multiclass learning with tilde o sqrt t regret"):
  with members:
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "first order regret bounds for combinatorial semi bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "perceptron mistake bounds"
    - "relative loss bounds for on line density estimation with the exponential family of distributions"
    - "efficient optimal learning for contextual bandits"

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)