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: "one pass auc optimization"
  Abstract: AUC is an important performance measure and many algorithms have been devoted to AUC optimization, mostly by minimizing a surrogate convex loss on a training data set. In this work, we focus on one-pass AUC optimization that requires only going through the training data once without storing the entire training dataset, where conventional online learning algorithms cannot be applied directly because AUC is measured by a sum of losses defined over pairs of instances from different classes. We develop a regression-based algorithm which only needs to maintain the first and second order statistics of training data in memory, resulting a storage requirement independent from the size of training data. To efficiently handle high dimensional data, we develop a randomized algorithm that approximates the covariance matrices by low rank matrices. We verify, both theoretically and empirically, the effectiveness of the proposed algorithm.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised learning meets ride sharing user experience enhancement"):
  with members:
    - "regularized learning for domain adaptation under label shifts"
    - "mba mini batch auc optimization"
    - "training set debugging using trusted items"
    - "learning to reweight examples for robust deep learning"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "xgboost a scalable tree boosting system"
    - "on the consistency of auc pairwise optimization"

Connected through hyperedge HE-2 (induced by source paper "stochastic auc maximization with deep neural networks"):
  with members:
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "an improved analysis of training over parameterized deep neural networks"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "an alternative view when does sgd escape local minima"
    - "a convergence analysis of gradient descent for deep linear neural networks"

Connected through hyperedge HE-3 (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"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "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-4 (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"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "stochastic optimization for machine learning"
    - "on the consistency of auc pairwise optimization"

Connected through hyperedge HE-5 (induced by source paper "quadruply stochastic gradient method for large scale nonlinear semi supervised ordinal regression auc optimization"):
  with members:
    - "quadruply stochastic gradients for large scale nonlinear semi supervised auc optimization"
    - "semi supervised ordinal regression based on empirical risk minimization"
    - "scalable semi supervised svm via triply stochastic gradients"
    - "scalable kernel methods via doubly stochastic gradients"
    - "on the consistency of auc pairwise optimization"

Connected through hyperedge HE-6 (induced by source paper "risk bounds for low cost bipartite ranking"):
  with members:
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "on the consistency of auc pairwise optimization"
    - "surrogate regret bounds for bipartite ranking via strongly proper losses"

Connected through hyperedge HE-7 (induced by source paper "automatic layout generation with applications in machine learning engine evaluation"):
  with members:
    - "epic efficient prediction of ic manufacturing hotspots with a unified meta classification formulation"

Connected through hyperedge HE-8 (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"

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)