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 theory approach to minimum error entropy criterion"
  Abstract: We consider the minimum error entropy (MEE) criterion and an empirical risk minimization learning algorithm in a regression setting. A learning theory approach is presented for this MEE algorithm and explicit error bounds are provided in terms of the approximation ability and capacity of the involved hypothesis space when the MEE scaling parameter is large. Novel asymptotic analysis is conducted for the generalization error associated with Renyi's entropy and a Parzen window function, to overcome technical difficulties arisen from the essential differences between the classical least squares problems and the MEE setting. A semi-norm and the involved symmetrized least squares error are introduced, which is related to some ranking algorithms.

Connected through hyperedge HE-1 (induced by source paper "learning with correntropy induced losses for regression with mixture of symmetric stable noise"):
  with members:
    - "learning without concentration"
    - "consistency analysis of an empirical minimum error entropy algorithm"
    - "robust hyperspectral unmixing with correntropy based metric"

Connected through hyperedge HE-2 (induced by source paper "stability and optimization error of stochastic gradient descent for pairwise learning"):
  with members:
    - "generalization error bounds for noisy iterative algorithms"
    - "data dependent stability of stochastic gradient descent"
    - "robustness and generalization for metric learning"
    - "train faster generalize better stability of stochastic gradient descent"
    - "one pass auc optimization"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "almost everywhere algorithmic stability and generalization error"
    - "random feature maps for dot product kernels"

Connected through hyperedge HE-3 (induced by source paper "unregularized online learning algorithms with general loss functions"):
  with members:
    - "consistency analysis of an empirical minimum error entropy algorithm"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "generalization bounds for metric and similarity learning"

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)