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: "active learning from positive and unlabeled data"
  Abstract: During recent years, active learning has evolved into a popular paradigm for utilizing user's feedback to improve accuracy of learning algorithms. Active learning works by selecting the most informative sample among unlabeled data and querying the label of that point from user. Many different methods such as uncertainty sampling and minimum risk sampling have been utilized to select the most informative sample in active learning. Although many active learning algorithms have been proposed so far, most of them work with binary or multi-class classification problems and therefore can not be applied to problems in which only samples from one class as well as a set of unlabeled data are available. Such problems arise in many real-world situations and are known as the problem of learning from positive and unlabeled data. In this paper we propose an active learning algorithm that can work when only samples of one class as well as a set of unlabeled data are available. Our method works by separately estimating probability density of positive and unlabeled points and then computing expected value of in formativeness to get rid of a hyper-parameter and have a better measure of in formativeness. Experiments and empirical analysis show promising results compared to other similar methods.

Connected through hyperedge HE-1 (induced by source paper "the data representativeness criterion predicting the performance of supervised classification based on data set similarity"):
  with members:
    - "representation learning a review and new perspectives"
    - "active learning with statistical models"

Connected through hyperedge HE-2 (induced by source paper "active learning of svdd hyperparameter values"):
  with members:
    - "an overview and a benchmark of active learning for outlier detection with one class classifiers"
    - "peak criterion for choosing gaussian kernel bandwidth in support vector data description"
    - "the mean and median criteria for kernel bandwidth selection for support vector data description"
    - "algorithms for learning kernels based on centered alignment"

Connected through hyperedge HE-3 (induced by source paper "an overview and a benchmark of active learning for outlier detection with one class classifiers"):
  with members:
    - "limitations of assessing active learning performance at runtime"
    - "the mythos of model interpretability"
    - "one class classification taxonomy of study and review of techniques"
    - "julia a fresh approach to numerical computing"
    - "toward supervised anomaly detection"

Connected through hyperedge HE-4 (induced by source paper "active learning for one class classification using two one class classifiers"):
  with members:
    - "one class classification taxonomy of study and review of techniques"

Connected through hyperedge HE-5 (induced by source paper "on the unreported profile is negative assumption for predictive cheminformatics"):
  with members:
    - "pu learning for matrix completion"
    - "bpr bayesian personalized ranking from implicit feedback"

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)