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 the importance of being improper"
  Abstract: Learning linear predictors with the logistic loss---both in stochastic and online settings---is a fundamental task in machine learning and statistics, with direct connections to classification and boosting. Existing "fast rates" for this setting exhibit exponential dependence on the predictor norm, and Hazan et al. (2014) showed that this is unfortunately unimprovable. Starting with the simple observation that the logistic loss is $1$-mixable, we design a new efficient improper learning algorithm for online logistic regression that circumvents the aforementioned lower bound with a regret bound exhibiting a doubly-exponential improvement in dependence on the predictor norm. This provides a positive resolution to a variant of the COLT 2012 open problem of McMahan and Streeter (2012) when improper learning is allowed. This improvement is obtained both in the online setting and, with some extra work, in the batch statistical setting with high probability. We also show that the improved dependence on predictor norm is near-optimal. #R##N#Leveraging this improved dependency on the predictor norm yields the following applications: (a) we give algorithms for online bandit multiclass learning with the logistic loss with an $\tilde{O}(\sqrt{n})$ relative mistake bound across essentially all parameter ranges, thus providing a solution to the COLT 2009 open problem of Abernethy and Rakhlin (2009), and (b) we give an adaptive algorithm for online multiclass boosting with optimal sample complexity, thus partially resolving an open problem of Beygelzimer et al. (2015) and Jung et al. (2017). Finally, we give information-theoretic bounds on the optimal rates for improper logistic regression with general function classes, thereby characterizing the extent to which our improvement for linear classes extends to other parametric and even nonparametric settings.

Connected through hyperedge HE-1 (induced by source paper "strength from weakness fast learning using weak supervision"):
  with members:
    - "weakly supervised disentanglement by pairwise similarities"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "generative discriminative complementary learning"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "self supervised learning of pretext invariant representations"
    - "unsupervised representation learning by predicting image rotations"
    - "exploring the limits of weakly supervised pretraining"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"

Connected through hyperedge HE-2 (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"
    - "from exp concavity to variance control high probability o 1 n rates and high probability online to batch conversion"
    - "logistic regression tight bounds for stochastic and online optimization"
    - "relative loss bounds for on line density estimation with the exponential family of distributions"

Connected through hyperedge HE-3 (induced by source paper "bandit multiclass linear classification efficient algorithms for the separable case"):
  with members:
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "learning neural networks with two nonlinear layers in polynomial time"
    - "learning depth three neural networks in polynomial time"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "bistro an efficient relaxation based method for contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "the price of bandit information in multiclass online classification"

Connected through hyperedge HE-4 (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"
    - "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"
    - "logistic regression tight bounds for stochastic and online optimization"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-5 (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"
    - "a tutorial on thompson sampling"
    - "learning without concentration"
    - "logistic regression tight bounds for stochastic and online optimization"
    - "rao blackwellised particle filtering for dynamic bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "contextual markov decision processes using generalized linear models"):
  with members:
    - "policy certificates towards accountable reinforcement learning"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "online stochastic linear optimization under one bit feedback"
    - "online learning in mdps with side information"

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)