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: "learning to classify with possible sensor failures"
  Abstract: In this paper, we propose an efficient algorithm to train a robust large-margin classifier, when corrupt measurements caused by sensor failure might be present in the training set. By incorporating a non-parametric prior based on the empirical distribution of the training data, we propose a Geometric-Entropy-Minimization regularized Maximum Entropy Discrimination (GEM-MED) method to perform classification and anomaly detection in a joint manner. We demonstrate that our proposed method can yield improved performance over previous robust classification methods in terms of both classification accuracy and anomaly detection rate using simulated data and real footstep data.

Connected through hyperedge HE-1 (induced by source paper "learning to bound the multi class bayes error"):
  with members:
    - "unsupervised meta learning for reinforcement learning"
    - "a dimension independent discriminant between distributions"
    - "scalable hash based estimation of divergence measures"
    - "direct estimation of information divergence using nearest neighbor ratios"
    - "information theoretic structure learning with confidence"
    - "improving convergence of divergence functional ensemble estimators"
    - "nonparametric ensemble estimation of distributional functionals"
    - "ensemble estimation of multivariate f divergence"

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)