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: "a feature selection method for multivariate performance measures"
  Abstract: Feature selection with specific multivariate performance measures is the key to the success of many applications such as image retrieval and text classification. The existing feature selection methods are usually designed for classification error. In this paper, we propose a generalized sparse regularizer. Based on the proposed regularizer, we present a unified feature selection framework for general loss functions. In particular, we study the novel feature selection paradigm by optimizing multivariate performance measures. The resultant formulation is a challenging problem for high-dimensional data. Hence, a two-layer cutting plane algorithm is proposed to solve this problem, and the convergence is presented. In addition, we adapt the proposed method to optimize multivariate measures for multiple-instance learning problems. The analyses by comparing with the state-of-the-art feature selection methods show that the proposed method is superior to others. Extensive experiments on large-scale and high-dimensional real-world datasets show that the proposed method outperforms l1-SVM and SVM-RFE when choosing a small subset of features, and achieves significantly improved performances over SVMperl in terms of F1-score.

Connected through hyperedge HE-1 (induced by source paper "estimating feature label dependence using gini distance statistics"):
  with members:
    - "screening tests for lasso problems"
    - "a semidefinite programming based search strategy for feature selection with mutual information measure"
    - "feature selection with conjunctions of decision stumps and learning from microarray data"

Connected through hyperedge HE-2 (induced by source paper "sparse learning of maximum likelihood model for optimization of complex loss function"):
  with members:
    - "supervised learning of sparse context reconstruction coefficients for data representation and classification"
    - "representing data by sparse combination of contextual data points for classification"
    - "image tag completion by local learning"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "multiple kernel multivariate performance learning using cutting plane algorithm"
    - "efficient optimization of performance measures by classifier adaptation"

Connected through hyperedge HE-3 (induced by source paper "multiple kernel multivariate performance learning using cutting plane algorithm"):
  with members:
    - "regularized maximum correntropy machine"
    - "image tag completion by local learning"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "efficient optimization of performance measures by classifier adaptation"
    - "multiple graph regularized protein domain ranking"

Connected through hyperedge HE-4 (induced by source paper "a novel multivariate performance optimization method based on sparse coding and hyper predictor learning"):
  with members:
    - "efficient optimization of performance measures by classifier adaptation"
    - "multiple graph regularized protein domain ranking"

Connected through hyperedge HE-5 (induced by source paper "multi view learning for multivariate performance measures optimization"):
  with members:
    - "multi view learning in the presence of view disagreement"

Connected through hyperedge HE-6 (induced by source paper "towards ultrahigh dimensional feature selection for big data"):
  with members:
    - "generalized fisher score for feature selection"
    - "efficient l1 lq norm regularization"
    - "feature hashing for large scale multitask learning"
    - "high dimensional non linear variable selection through hierarchical kernel learning"
    - "sparse online learning via truncated gradient"

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)