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 "calibrated surrogate maximization of linear fractional utility in binary classification"):
  Abstract: Complex classification performance metrics such as the F${}_\beta$-measure and Jaccard index are often used, in order to handle class-imbalanced cases such as information retrieval and image segmentation. These performance metrics are not decomposable, that is, they cannot be expressed in a per-example manner, which hinders a straightforward application of the M-estimation widely used in supervised learning. In this paper, we consider \emph{linear-fractional metrics}, which are a family of classification performance metrics that encompasses many standard metrics such as the F${}_\beta$-measure and Jaccard index, and propose methods to directly maximize performances under those metrics. A clue to tackle their direct optimization is a \emph{calibrated surrogate utility}, which is a tractable lower bound of the true utility function representing a given metric. We characterize necessary conditions which make the surrogate maximization coincide with the maximization of the true utility. To the best of our knowledge, this is the first surrogate calibration analysis for the linear-fractional metrics. We also propose gradient-based optimization algorithms and show their practical usefulness in experiments.

Includes members:
  Member "on structured prediction theory with calibrated convex surrogate losses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "strategic prediction with latent aggregative games")
  Member "beyond convexity stochastic quasi convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "on the consistency of auc pairwise optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised learning meets ride sharing user experience enhancement")
  Member "optimizing f measure a tale of two approaches":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimizing black box metrics with adaptive surrogates")
  Member "agnostic learning of monomials by halfspaces is hard":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "solving empirical risk minimization in the current matrix multiplication time")

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)