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: "an online boosting algorithm with theoretical justifications"
  Abstract: We study the task of online boosting--combining online weak learners into an online strong learner. While batch boosting has a sound theoretical foundation, online boosting deserves more study from the theoretical perspective. In this paper, we carefully compare the differences between online and batch boosting, and propose a novel and reasonable assumption for the online weak learner. Based on the assumption, we design an online boosting algorithm with a strong theoretical guarantee by adapting from the offline SmoothBoost algorithm that matches the assumption closely. We further tackle the task of deciding the number of weak learners using established theoretical results for online convex programming and predicting with expert advice. Experiments on real-world data sets demonstrate that the proposed algorithm compares favorably with existing online boosting algorithms.

Connected through hyperedge HE-1 (induced by source paper "online agnostic boosting via regret minimization"):
  with members:
    - "boosting for dynamical systems"
    - "passing tests without memorizing two models for fooling discriminators"
    - "introduction to online convex optimization"
    - "online control with adversarial disturbances"
    - "the role of interactivity in local differential privacy"
    - "private learning implies online learning an efficient reduction"
    - "logarithmic regret for online control"
    - "the nonstochastic control problem"

Connected through hyperedge HE-2 (induced by source paper "deep metric learning with bier boosting independent embeddings robustly"):
  with members:
    - "incremental boosting convolutional neural network for facial action unit recognition"
    - "adversarial discriminative domain adaptation"
    - "deep metric learning with angular loss"
    - "no fuss distance metric learning using proxies"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "smart mining for deep metric learning"
    - "siamese instance search for tracking"
    - "deep metric learning via facility location"

Connected through hyperedge HE-3 (induced by source paper "online gradient boosting"):
  with members:
    - "optimal and adaptive algorithms for online boosting"
    - "boosting with the logistic loss is consistent"

Connected through hyperedge HE-4 (induced by source paper "optimal and adaptive algorithms for online boosting"):
  with members:
    - "a drifting games analysis for online learning and applications to boosting"

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)