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: "logistic regression tight bounds for stochastic and online optimization"
  Abstract: The logistic loss function is often advocated in machine learning and statistics as a smooth and strictly convex surrogate for the 0-1 loss. In this paper we investigate the question of whether these smoothness and convexity properties make the logistic loss preferable to other widely considered options such as the hinge loss. We show that in contrast to known asymptotic bounds, as long as the number of prediction/optimization iterations is sub exponential, the logistic loss provides no improvement over a generic non-smooth loss function such as the hinge loss. In particular we show that the convergence rate of stochastic logistic optimization is bounded from below by a polynomial in the diameter of the decision set and the number of prediction iterations, and provide a matching tight upper bound. This resolves the COLT open problem of McMahan and Streeter (2012).

Connected through hyperedge HE-1 (induced by source paper "efficient improper learning for online logistic regression"):
  with members:
    - "introduction to online convex optimization"
    - "beyond least squares fast rates for regularized empirical risk minimization through self concordance"
    - "logistic regression the importance of being improper"
    - "from exp concavity to variance control high probability o 1 n rates and high probability online to batch conversion"
    - "relative loss bounds for on line density estimation with the exponential family of distributions"

Connected through hyperedge HE-2 (induced by source paper "logistic regression regret what s the catch"):
  with members:
    - "a survey of algorithms and analysis for adaptive online learning"
    - "relative loss bounds for on line density estimation with the exponential family of distributions"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-3 (induced by source paper "beyond least squares fast rates for regularized empirical risk minimization through self concordance"):
  with members:
    - "statistical optimality of stochastic gradient descent on hard learning problems through multiple passes"
    - "logistic regression the importance of being improper"
    - "large scale kernel learning using block coordinate descent"
    - "fast rates with high probability in exp concave statistical learning"
    - "fast rates in statistical and online learning"
    - "on the complexity of learning with kernels"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-4 (induced by source paper "online sampling from log concave distributions"):
  with members:
    - "introduction to online convex optimization"
    - "simulated tempering langevin monte carlo ii an improved proof using soft markov chain decomposition"
    - "logistic regression the importance of being improper"
    - "a tutorial on thompson sampling"
    - "learning without concentration"
    - "rao blackwellised particle filtering for dynamic bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "logistic regression the importance of being improper"):
  with members:
    - "fast rates with high probability in exp concave statistical learning"
    - "from exp concavity to variance control high probability o 1 n rates and high probability online to batch conversion"
    - "fast rates in statistical and online learning"
    - "sequential probability assignment with binary alphabets and large classes of experts"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-6 (induced by source paper "parameter free online learning via model selection"):
  with members:
    - "introduction to online convex optimization"
    - "online learning without prior information"
    - "coin betting and parameter free online learning"
    - "fast rates in statistical and online learning"
    - "simultaneous model selection and optimization through parameter free stochastic learning"
    - "follow the leader if you can hedge if you must"
    - "on the universality of online mirror descent"
    - "a parameter free hedging algorithm"

Connected through hyperedge HE-7 (induced by source paper "fast rates for bandit optimization with upper confidence frank wolfe"):
  with members:
    - "bandits with concave rewards and convex knapsacks"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "on the complexity of bandit and derivative free stochastic convex optimization"

Connected through hyperedge HE-8 (induced by source paper "highly smooth zero th order online optimization vianney perchet"):
  with members:
    - "on zeroth order stochastic convex optimization via random walks"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "convergence rates of inexact proximal gradient methods for convex optimization"
    - "self concordant analysis for logistic regression"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

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)