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: "optimal and adaptive algorithms for online boosting"
  Abstract: We study online boosting, the task of converting any weak online learner into a strong online learner. Based on a novel and natural definition of weak online learnability, we develop two online boosting algorithms. The first algorithm is an online version of boost-by-majority. By proving a matching lower bound, we show that this algorithm is essentially optimal in terms of the number of weak learners and the sample complexity needed to achieve a specified accuracy. This optimal algorithm is not adaptive however. Using tools from online loss minimization, we derive an adaptive online boosting algorithm that is also parameter-free, but not optimal. Both algorithms work with base learners that can handle example importance weights directly, as well as by rejection sampling examples with probability defined by the booster. Results are complemented with an extensive experimental study.

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 "private learning implies online learning an efficient reduction"):
  with members:
    - "passing tests without memorizing two models for fooling discriminators"
    - "efficient private algorithms for learning halfspaces"
    - "private pac learning implies finite littlestone dimension"
    - "learning in non convex games with an optimization oracle"
    - "how to use heuristics for differential privacy"
    - "calibrating noise to variance in adaptive data analysis"
    - "oracle efficient online learning and auction design"
    - "differentially private release and learning of threshold functions"

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 "exponentially convergent stochastic k pca without variance reduction"):
  with members:
    - "convergence of stochastic gradient descent for pca"
    - "the fast convergence of incremental pca"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"
    - "global convergence of stochastic gradient descent for some non convex matrix problems"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "the noisy power method a meta algorithm with applications"
    - "matrix completion from a few entries"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "construction of efficient detectors for character information recognition"):
  with members:
    - "license plate detection and recognition using deeply learned convolutional neural networks"

Connected through hyperedge HE-6 (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-7 (induced by source paper "online machine learning in big data streams"):
  with members:
    - "computing web scale topic models using an asynchronous parameter server"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "clipper a low latency online prediction serving system"
    - "xgboost a scalable tree boosting system"
    - "general factorization framework for context aware recommendations"
    - "vht vertical hoeffding tree"
    - "evaluation of recommender systems in streaming environments"
    - "massively parallel methods for deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "handling concept drift via model reuse"):
  with members:
    - "hybrid forest a concept drift aware data stream mining algorithm"
    - "introduction to online convex optimization"
    - "robust artificial intelligence and robust human organizations"
    - "adaptive online learning in dynamic environments"
    - "learn on source refine on target a model transfer learning framework with random forests"
    - "concept drift adaptation by exploiting historical knowledge"
    - "fast rates by transferring from auxiliary hypotheses"
    - "a vector contraction inequality for rademacher complexities"

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)