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: "unregularized online learning algorithms with general loss functions"
  Abstract: In this paper, we consider unregularized online learning algorithms in a Reproducing Kernel Hilbert Spaces (RKHS). Firstly, we derive explicit convergence rates of the unregularized online learning algorithms for classification associated with a general gamma-activating loss (see Definition 1 in the paper). Our results extend and refine the results in Ying and Pontil (2008) for the least-square loss and the recent result in Bach and Moulines (2011) for the loss function with a Lipschitz-continuous gradient. Moreover, we establish a very general condition on the step sizes which guarantees the convergence of the last iterate of such algorithms. Secondly, we establish, for the first time, the convergence of the unregularized pairwise learning algorithm with a general loss function and derive explicit rates under the assumption of polynomially decaying step sizes. Concrete examples are used to illustrate our main results. The main techniques are tools from convex analysis, refined inequalities of Gaussian averages, and an induction approach.

Connected through hyperedge HE-1 (induced by source paper "stochastic iterative hard thresholding for graph structured sparsity optimization"):
  with members:
    - "dual iterative hard thresholding from non convex sparse minimization to non smooth concave maximization"
    - "how to escape saddle points efficiently"
    - "a survey on social media anomaly detection"
    - "train faster generalize better stability of stochastic gradient descent"
    - "adam a method for stochastic optimization"
    - "approximation algorithms for model based compressive sensing"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-2 (induced by source paper "dual averaging method for online graph structured sparsity"):
  with members:
    - "introduction to online convex optimization"
    - "learning in non convex games with an optimization oracle"
    - "efficient regret minimization in non convex games"
    - "graph sparse logistic regression"
    - "learning with feature evolvable streams"
    - "adam a method for stochastic optimization"
    - "approximation algorithms for model based compressive sensing"
    - "gradient hard thresholding pursuit for sparsity constrained optimization"

Connected through hyperedge HE-3 (induced by source paper "deep neural networks for rotation invariance approximation and learning"):
  with members:
    - "realizing data features by deep nets"
    - "generalization and expressivity for deep nets"
    - "construction of neural networks for realization of localized deep learning"
    - "universality of deep convolutional neural networks"
    - "deep radial kernel networks approximating radially symmetric functions with deep networks"
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "deep nets for local manifold learning"

Connected through hyperedge HE-4 (induced by source paper "stochastic gradient descent for nonconvex learning without bounded gradient assumptions"):
  with members:
    - "convergence of online mirror descent"
    - "uniform convergence of gradients for non convex learning and optimization"
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "convergence of unregularized online learning algorithms"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "fast polynomial kernel classification for massive data"):
  with members:
    - "learning rates for classification with gaussian kernels"
    - "training neural networks without gradients a scalable admm approach"
    - "distributed learning with regularized least squares"
    - "going deeper with convolutions"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-6 (induced by source paper "convergence of online mirror descent"):
  with members:
    - "distributed learning with regularized least squares"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "making gradient descent optimal for strongly convex stochastic optimization"
    - "regularization techniques for learning with matrices"

Connected through hyperedge HE-7 (induced by source paper "convergence of online mirror descent algorithms"):
  with members:
    - "distributed learning with regularized least squares"
    - "regularization techniques for learning with matrices"

Connected through hyperedge HE-8 (induced by source paper "fast and strong convergence of online learning algorithms"):
  with members:
    - "statistical optimality of stochastic gradient descent on hard learning problems through multiple passes"
    - "convergence of unregularized online learning algorithms"
    - "optimal learning for multi pass stochastic gradient methods"
    - "distributed learning with regularized least squares"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "making gradient descent optimal for strongly convex stochastic optimization"

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)