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 doubt about margin explanation of boosting"
  Abstract: Margin theory provides one of the most popular explanations to the success of \texttt{AdaBoost}, where the central point lies in the recognition that \textit{margin} is the key for characterizing the performance of \texttt{AdaBoost}. This theory has been very influential, e.g., it has been used to argue that \texttt{AdaBoost} usually does not overfit since it tends to enlarge the margin even after the training error reaches zero. Previously the \textit{minimum margin bound} was established for \texttt{AdaBoost}, however, \cite{Breiman1999} pointed out that maximizing the minimum margin does not necessarily lead to a better generalization. Later, \cite{Reyzin:Schapire2006} emphasized that the margin distribution rather than minimum margin is crucial to the performance of \texttt{AdaBoost}. In this paper, we first present the \textit{$k$th margin bound} and further study on its relationship to previous work such as the minimum margin bound and Emargin bound. Then, we improve the previous empirical Bernstein bounds \citep{Maurer:Pontil2009,Audibert:Munos:Szepesvari2009}, and based on such findings, we defend the margin-based explanation against Breiman's doubts by proving a new generalization error bound that considers exactly the same factors as \cite{Schapire:Freund:Bartlett:Lee1998} but is sharper than \cite{Breiman1999}'s minimum margin bound. By incorporating factors such as average margin and variance, we present a generalization error bound that is heavily related to the whole margin distribution. We also provide margin distribution bounds for generalization error of voting classifiers in finite VC-dimension space.

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 "forest representation learning guided by margin distribution"):
  with members:
    - "multi layered gradient boosting decision trees"
    - "deep forest"
    - "deep forest towards an alternative to deep neural networks"
    - "learning deep resnet blocks sequentially using boosting theory"
    - "autoencoder by forest"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-3 (induced by source paper "learning near optimal convex combinations of basis models with generalization guarantees"):
  with members:
    - "boosted generative models"
    - "adam a method for stochastic optimization"
    - "breaking the curse of dimensionality with convex neural networks"

Connected through hyperedge HE-4 (induced by source paper "margin based generalization lower bounds for boosted classifiers"):
  with members:
    - "optimal minimal margin maximization with boosting"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-5 (induced by source paper "maximal margin distribution support vector regression with coupled constraints based convex optimization"):
  with members:
    - "maximum margin multiclass nearest neighbors"
    - "large margin distribution machine"

Connected through hyperedge HE-6 (induced by source paper "improving generalization of deep neural networks by leveraging margin distribution"):
  with members:
    - "stronger generalization bounds for deep nets via a compression approach"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "spectrally normalized margin bounds for neural networks"
    - "learning deep resnet blocks sequentially using boosting theory"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "norm based capacity control in neural networks"

Connected through hyperedge HE-7 (induced by source paper "less but better generalization enhancement of ordinal embedding via distributional margin"):
  with members:
    - "optimal margin distribution machine"
    - "learning concept embeddings with combined human machine expertise"
    - "efficient online relative comparison kernel learning"
    - "cost effective hits for relative similarity comparisons"
    - "large margin distribution machine"
    - "adaptively learning the crowd kernel"

Connected through hyperedge HE-8 (induced by source paper "effects of the optimisation of the margin distribution on generalisation in deep architectures"):
  with members:
    - "optimal margin distribution machine"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "large margin distribution machine"

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)