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: "self concordant analysis for logistic regression"
  Abstract: Most of the non-asymptotic theoretical work in regression is carried out for the square loss, where estimators can be obtained through closed-form expressions. In this paper, we use and extend tools from the convex optimization literature, namely self-concordant functions, to provide simple extensions of theoretical results for the square loss to the logistic loss. We apply the extension techniques to logistic regression with regularization by the $\ell_2$-norm and regularization by the $\ell_1$-norm, showing that new results for binary classification through logistic regression can be easily derived from corresponding results for least-squares regression.

Connected through hyperedge HE-1 (induced by source paper "the implicit and explicit regularization effects of dropout"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "kernel and deep regimes in overparametrized models"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "transformer xl attentive language models beyond a fixed length context"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "implicit regularization in deep matrix factorization"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"

Connected through hyperedge HE-2 (induced by source paper "logistic regression regret what s the catch"):
  with members:
    - "logistic regression tight bounds for stochastic and online optimization"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "improved optimistic algorithms for logistic bandits"):
  with members:
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "learning to optimize via posterior sampling"
    - "finite time analysis of kernelised contextual bandits"

Connected through hyperedge HE-4 (induced by source paper "fast and furious convergence stochastic second order methods under interpolation"):
  with members:
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "beyond least squares fast rates for regularized empirical risk minimization through self concordance"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "global linear convergence of newton s method without strong convexity or lipschitz gradients"
    - "on the acceleration of l bfgs with second order information and stochastic batches"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "adaptive newton method for empirical risk minimization to statistical accuracy"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-5 (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"
    - "logistic regression tight bounds for stochastic and online optimization"

Connected through hyperedge HE-6 (induced by source paper "a dynamic sampling adaptive sgd method for machine learning"):
  with members:
    - "shampoo preconditioned stochastic tensor optimization"
    - "improving generalization performance by switching from adam to sgd"
    - "deep residual learning for image recognition"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (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"
    - "logistic regression tight bounds for stochastic and online optimization"
    - "contextual bandit algorithms with supervised learning guarantees"

Connected through hyperedge HE-8 (induced by source paper "a distributed second order algorithm you can trust"):
  with members:
    - "giant globally improved approximate newton method for distributed optimization"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "a general distributed dual coordinate optimization framework for regularized loss minimization"
    - "a distributed block coordinate descent method for training l_1 regularized linear classifiers"
    - "communication efficient distributed optimization using an approximate newton type method"

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)