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 "sparse learning of maximum likelihood model for optimization of complex loss function"):
  Abstract: Traditional machine learning methods usually minimize a simple loss function to learn a predictive model, and then use a complex performance measure to measure the prediction performance. However, minimizing a simple loss function cannot guarantee that an optimal performance. In this paper, we study the problem of optimizing the complex performance measure directly to obtain a predictive model. We proposed to construct a maximum likelihood model for this problem, and to learn the model parameter, we minimize a com- plex loss function corresponding to the desired complex performance measure. To optimize the loss function, we approximate the upper bound of the complex loss. We also propose impose the sparsity to the model parameter to obtain a sparse model. An objective is constructed by combining the upper bound of the loss function and the sparsity of the model parameter, and we develop an iterative algorithm to minimize it by using the fast iterative shrinkage- thresholding algorithm framework. The experiments on optimization on three different complex performance measures, including F-score, receiver operating characteristic curve, and recall precision curve break even point, over three real-world applications, aircraft event recognition of civil aviation safety, in- trusion detection in wireless mesh networks, and image classification, show the advantages of the proposed method over state-of-the-art methods.

Includes members:
  Member "supervised learning of sparse context reconstruction coefficients for data representation and classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimizing top precision performance measure of content based image retrieval by learning similarity function")
  Member "representing data by sparse combination of contextual data points for classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse coding with earth mover s distance for multi instance histogram representation")
  Member "image tag completion by local learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimizing top precision performance measure of content based image retrieval by learning similarity function")
  Member "supervised cross modal factor analysis for multiple modal data classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimizing top precision performance measure of content based image retrieval by learning similarity function")
  Member "multiple kernel multivariate performance learning using cutting plane algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimizing top precision performance measure of content based image retrieval by learning similarity function")
  Member "a feature selection method for multivariate performance measures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "estimating feature label dependence using gini distance statistics")
  Member "efficient optimization of performance measures by classifier adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "handling concept drift via model reuse")

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)