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: "large margin distribution machine"
  Abstract: Support vector machine (SVM) has been one of the most popular learning algorithms, with the central idea of maximizing the minimum margin, i.e., the smallest distance from the instances to the classification boundary. Recent theoretical results, however, disclosed that maximizing the minimum margin does not necessarily lead to better generalization performances, and instead, the margin distribution has been proven to be more crucial. In this paper, we propose the Large margin Distribution Machine (LDM), which tries to achieve a better generalization performance by optimizing the margin distribution. We characterize the margin distribution by the first- and second-order statistics, i.e., the margin mean and variance. The LDM is a general learning approach which can be used in any place where SVM can be applied, and its superiority is verified both theoretically and empirically in this paper.

Connected through hyperedge HE-1 (induced by source paper "maximal margin distribution support vector regression with coupled constraints based convex optimization"):
  with members:
    - "maximum margin multiclass nearest neighbors"
    - "on the doubt about margin explanation of boosting"

Connected through hyperedge HE-2 (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"
    - "adaptively learning the crowd kernel"
    - "on the doubt about margin explanation of boosting"

Connected through hyperedge HE-3 (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"
    - "on the doubt about margin explanation of boosting"

Connected through hyperedge HE-4 (induced by source paper "optimal margin distribution machine"):
  with members:
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "mini batch primal and dual methods for svms"
    - "block coordinate frank wolfe optimization for structural svms"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "towards optimal one pass large scale learning with averaged stochastic gradient descent"
    - "on the doubt about margin explanation of boosting"

Connected through hyperedge HE-5 (induced by source paper "e distance weighted support vector regression"):
  with members:
    - "towards optimal one pass large scale learning with averaged stochastic gradient descent"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-6 (induced by source paper "extreme entropy machines robust information theoretic classification"):
  with members:
    - "multithreshold entropy linear classifier"
    - "cross entropy clustering"
    - "cluster based rbf kernel for support vector machines"
    - "the numpy array a structure for efficient numerical computation"

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)