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: "on the generalization ability of online learning algorithms for pairwise loss functions"
  Abstract: In this paper, we study the generalization properties of online learning based stochastic methods for supervised learning problems where the loss function is dependent on more than one training sample (e.g., metric learning, ranking). We present a generic decoupling technique that enables us to provide Rademacher complexity-based generalization error bounds. Our bounds are in general tighter than those obtained by Wang et al (COLT 2012) for the same problem. Using our decoupling technique, we are further able to obtain fast convergence rates for strongly convex pairwise loss functions. We are also able to analyze a class of memory efficient online learning algorithms for pairwise learning problems that use only a bounded subset of past training samples to update the hypothesis at each step. Finally, in order to complement our generalization bounds, we propose a novel memory efficient online learning algorithm for higher order learning problems with bounded regret guarantees.

Connected through hyperedge HE-1 (induced by source paper "stability and optimization error of stochastic gradient descent for pairwise learning"):
  with members:
    - "generalization error bounds for noisy iterative algorithms"
    - "data dependent stability of stochastic gradient descent"
    - "robustness and generalization for metric learning"
    - "train faster generalize better stability of stochastic gradient descent"
    - "one pass auc optimization"
    - "almost everywhere algorithmic stability and generalization error"
    - "random feature maps for dot product kernels"
    - "learning theory approach to minimum error entropy criterion"

Connected through hyperedge HE-2 (induced by source paper "stochastic proximal auc maximization"):
  with members:
    - "introduction to online convex optimization"
    - "stochastic variance reduction methods for saddle point problems"
    - "support vector algorithms for optimizing the partial area under the roc curve"
    - "simultaneous model selection and optimization through parameter free stochastic learning"
    - "one pass auc optimization"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "stochastic optimization for machine learning"
    - "on the consistency of auc pairwise optimization"

Connected through hyperedge HE-3 (induced by source paper "risk bounds for low cost bipartite ranking"):
  with members:
    - "one pass auc optimization"
    - "on the consistency of auc pairwise optimization"
    - "surrogate regret bounds for bipartite ranking via strongly proper losses"

Connected through hyperedge HE-4 (induced by source paper "online learning a comprehensive survey"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "a survey of multi view representation learning"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "online deep learning learning deep neural networks on the fly"
    - "an online hierarchical algorithm for extreme clustering"
    - "the scaling limit of high dimensional online independent component analysis"
    - "efficient second order online learning by sketching"
    - "a framework of sparse online learning and its applications"

Connected through hyperedge HE-5 (induced by source paper "learning in non convex games with an optimization oracle"):
  with members:
    - "introduction to online convex optimization"
    - "on convergence and stability of gans"
    - "efficient regret minimization in non convex games"
    - "fast rates for empirical risk minimization of strict saddle problems"
    - "oracle efficient online learning and auction design"
    - "the computational power of optimization in online learning"
    - "prediction by random walk perturbation"

Connected through hyperedge HE-6 (induced by source paper "adaptive subgradient methods for online auc maximization"):
  with members:
    - "one pass auc optimization"
    - "on the consistency of auc pairwise optimization"
    - "sparse online learning via truncated gradient"

Connected through hyperedge HE-7 (induced by source paper "confidence weighted bipartite ranking"):
  with members:
    - "top rank optimization in linear time"
    - "one pass auc optimization"
    - "exact soft confidence weighted learning"

Connected through hyperedge HE-8 (induced by source paper "a sparse nonlinear classifier design using auc optimization"):
  with members:
    - "adaptive subgradient methods for online auc maximization"
    - "one pass auc 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)