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: "online gradient boosting"
  Abstract: We extend the theory of boosting for regression problems to the online learning setting. Generalizing from the batch setting for boosting, the notion of a weak learning algorithm is modeled as an online learning algorithm with linear loss functions that competes with a base class of regression functions, while a strong learning algorithm is an online learning algorithm with convex loss functions that competes with a larger class of regression functions. Our main result is an online gradient boosting algorithm which converts a weak online learning algorithm into a strong one where the larger class of functions is the linear span of the base class. We also give a simpler boosting algorithm that converts a weak online learning algorithm into a strong one where the larger class of functions is the convex hull of the base class, and prove its optimality.

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 "boosting simple learners"):
  with members:
    - "boosting for dynamical systems"

Connected through hyperedge HE-3 (induced by source paper "boosting for dynamical systems"):
  with members:
    - "introduction to online convex optimization"
    - "online control with adversarial disturbances"
    - "spectral filtering for general linear dynamical systems"
    - "online linear quadratic control"
    - "model free linear quadratic control via reduction to expert prediction"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "global convergence of policy gradient methods for linearized control problems"
    - "learning linear dynamical systems via spectral filtering"

Connected through hyperedge HE-4 (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-5 (induced by source paper "gradient boosting on stochastic data streams"):
  with members:
    - "optimal and adaptive algorithms for online boosting"
    - "convolutional channel features"
    - "adam a method for stochastic optimization"
    - "generalized boosting algorithms for convex optimization"

Connected through hyperedge HE-6 (induced by source paper "learning infinite rbms with frank wolfe"):
  with members:
    - "breaking the curse of dimensionality with convex neural networks"
    - "block coordinate frank wolfe optimization for structural svms"

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)