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 dual formulation of boosting algorithms"
  Abstract: We study boosting algorithms from a new perspective. We show that the Lagrange dual problems of l1-norm-regularized AdaBoost, LogitBoost, and soft-margin LPBoost with generalized hinge loss are all entropy maximization problems. By looking at the dual problems of these boosting algorithms, we show that the success of boosting algorithms can be understood in terms of maintaining a better margin distribution by maximizing margins and at the same time controlling the margin variance. We also theoretically prove that approximately, l1-norm-regularized AdaBoost maximizes the average margin, instead of the minimum margin. The duality formulation also enables us to develop column-generation-based optimization algorithms, which are totally corrective. We show that they exhibit almost identical classification results to that of standard stagewise additive boosting algorithms but with much faster convergence rates. Therefore, fewer weak classifiers are needed to build the ensemble using our proposed optimization technique.

Connected through hyperedge HE-1 (induced by source paper "margin maximization as lossless maximal compression"):
  with members:
    - "training behavior of deep neural network in frequency domain"
    - "chaining mutual information and tightening generalization bounds"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "opening the black box of deep neural networks via information"
    - "a brief introduction to machine learning for engineers"
    - "deep variational information bottleneck"

Connected through hyperedge HE-2 (induced by source paper "fully decentralized joint learning of personalized models and collaboration graphs"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "djam distributed jacobi asynchronous method for learning personal models"
    - "personalized and private peer to peer machine learning"
    - "federated multi task learning"
    - "decentralized collaborative learning of personalized models over networks"
    - "communication efficient learning of deep networks from decentralized data"
    - "asynchronous multi task learning"
    - "distributed multi task learning with shared representation"

Connected through hyperedge HE-3 (induced by source paper "boosting with lexicographic programming addressing class imbalance without cost tuning"):
  with members:
    - "rethinking the inception architecture for computer vision"
    - "spectral unmixing via data guided sparsity"
    - "double base asymmetric adaboost"

Connected through hyperedge HE-4 (induced by source paper "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities"):
  with members:
    - "a tight convex upper bound on the likelihood of a finite mixture"
    - "on the chi square and higher order chi distances for approximating f divergences"
    - "renyi divergence and kullback leibler divergence"
    - "a closed form expression for the sharma mittal entropy of exponential families"
    - "on renyi and tsallis entropies and divergences for exponential families"
    - "the burbea rao and bhattacharyya centroids"
    - "constructing two dimensional voronoi diagrams via divide and conquer of envelopes in space"
    - "statistical exponential families a digest with flash cards"

Connected through hyperedge HE-5 (induced by source paper "structured learning of binary codes with column generation"):
  with members:
    - "fast supervised hashing with decision trees for high dimensional data"
    - "optimizing ranking measures for compact binary code learning"
    - "learning hash functions using column generation"
    - "inductive hashing on manifolds"
    - "a general two step approach to learning based hashing"
    - "structboost boosting methods for predicting structured output variables"
    - "positive semidefinite metric learning using boosting like algorithms"

Connected through hyperedge HE-6 (induced by source paper "a distributed frank wolfe algorithm for communication efficient sparse learning"):
  with members:
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "block coordinate frank wolfe optimization for structural svms"
    - "distributed learning communication complexity and privacy"
    - "a unified framework for approximating and clustering data"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-7 (induced by source paper "optimizing ranking measures for compact binary code learning"):
  with members:
    - "fast supervised hashing with decision trees for high dimensional data"
    - "learning hash functions using column generation"
    - "inductive hashing on manifolds"
    - "a general two step approach to learning based hashing"
    - "structboost boosting methods for predicting structured output variables"

Connected through hyperedge HE-8 (induced by source paper "from kernel machines to ensemble learning"):
  with members:
    - "boosting through optimization of margin distributions"

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)