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: "efficient optimization of performance measures by classifier adaptation"
  Abstract: In practical applications, machine learning algorithms are often needed to learn classifiers that optimize domain specific performance measures. Previously, the research has focused on learning the needed classifier in isolation, yet learning nonlinear classifier for nonlinear and nonsmooth performance measures is still hard. In this paper, rather than learning the needed classifier by optimizing specific performance measure directly, we circumvent this problem by proposing a novel two-step approach called CAPO, namely, to first train nonlinear auxiliary classifiers with existing learning methods and then to adapt auxiliary classifiers for specific performance measures. In the first step, auxiliary classifiers can be obtained efficiently by taking off-the-shelf learning algorithms. For the second step, we show that the classifier adaptation problem can be reduced to a quadratic program problem, which is similar to linear $({\rm SVM}^{\rm perf})$ and can be efficiently solved. By exploiting nonlinear auxiliary classifiers, CAPO can generate nonlinear classifier which optimizes a large variety of performance measures, including all the performance measures based on the contingency table and AUC, while keeping high computational efficiency. Empirical studies show that CAPO is effective and of high computational efficiency, and it is even more efficient than linear $({\rm SVM}^{\rm perf})$.

Connected through hyperedge HE-1 (induced by source paper "handling concept drift via model reuse"):
  with members:
    - "hybrid forest a concept drift aware data stream mining algorithm"
    - "introduction to online convex optimization"
    - "robust artificial intelligence and robust human organizations"
    - "adaptive online learning in dynamic environments"
    - "learn on source refine on target a model transfer learning framework with random forests"
    - "concept drift adaptation by exploiting historical knowledge"
    - "fast rates by transferring from auxiliary hypotheses"
    - "a vector contraction inequality for rademacher complexities"

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"
    - "a feature selection method for multivariate performance measures"

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"
    - "a feature selection method for multivariate performance measures"
    - "multiple graph regularized protein domain ranking"

Connected through hyperedge HE-4 (induced by source paper "online classifier adaptation for cost sensitive learning"):
  with members:
    - "characterizing honeypot captured cyber attacks statistical framework and case study"
    - "a characterization of cybersecurity posture from network telescope data"
    - "an evasion and counter evasion study in malicious websites detection"
    - "multiple graph regularized protein domain ranking"

Connected through hyperedge HE-5 (induced by source paper "a novel multivariate performance optimization method based on sparse coding and hyper predictor learning"):
  with members:
    - "a feature selection method for multivariate performance measures"
    - "multiple graph regularized protein domain ranking"

Connected through hyperedge HE-6 (induced by source paper "top rank optimization in linear time"):
  with members:
    - "one pass auc optimization"
    - "sparse support vector infinite push"
    - "direct optimization of ranking measures"

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)