human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "robust logistic regression against attribute and label outliers via restricted minimum error entropy criterion"):
  Abstract: Minimum Error Entropy (MEE) criterion has been verified as a powerful approach for robust learning in the field of Information Theoretic Learning. The purpose of this study is to demonstrate the great potential of MEE in robust classification, which is a relative vacancy in the literature. Based on the logistic regression model, we analyze the optimal error distribution with outliers, and implement restriction with a specially designed quantization codebook to avoid possible failure caused by the original MEE. The performance of the restricted MEE based logistic regression, called LR-RMEE, is experimentally evaluated on toy examples and public datasets. The results demonstrate that the novel robust variant can outperform existing methods in most cases. Moreover, the proposed restricted MEE criterion can be applied to other classifiers for corresponding robust variants, revealing desirable effectiveness and generality.

Includes members:
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "robust logistic regression using shift parameters long version":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust logistic regression against attribute and label outliers via information theoretic learning")
  Member "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")

Question: Please tell me which class the source paper 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)