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 "beyond least squares fast rates for regularized empirical risk minimization through self concordance"):
  Abstract: We consider learning methods based on the regularization of a convex empirical risk by a squared Hilbertian norm, a setting that includes linear predictors and non-linear predictors through positive-definite kernels. In order to go beyond the generic analysis leading to convergence rates of the excess risk as $O(1/\sqrt{n})$ from $n$ observations, we assume that the individual losses are self-concordant, that is, their third-order derivatives are bounded by their second-order derivatives. This setting includes least-squares, as well as all generalized linear models such as logistic and softmax regression. For this class of losses, we provide a bias-variance decomposition and show that the assumptions commonly made in least-squares regression, such as the source and capacity conditions, can be adapted to obtain fast non-asymptotic rates of convergence by improving the bias terms, the variance terms or both.

Includes members:
  Member "statistical optimality of stochastic gradient descent on hard learning problems through multiple passes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate")
  Member "logistic regression the importance of being improper":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "strength from weakness fast learning using weak supervision")
  Member "large scale kernel learning using block coordinate descent":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low precision random fourier features for memory constrained kernel approximation")
  Member "fast rates with high probability in exp concave statistical learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "strength from weakness fast learning using weak supervision")
  Member "fast rates in statistical and online learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "strength from weakness fast learning using weak supervision")
  Member "on the complexity of learning with kernels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "correlation clustering with adaptive similarity queries")
  Member "logistic regression tight bounds for stochastic and online optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient improper learning for online logistic regression")
  Member "self concordant analysis for logistic regression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the implicit and explicit regularization effects of dropout")

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)