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 "semi supervised classification based on classification from positive and unlabeled data"):
  Abstract: Most of the semi-supervised classification methods developed so far use unlabeled data for regularization purposes under particular distributional assumptions such as the cluster assumption. In contrast, recently developed methods of classification from positive and unlabeled data (PU classification) use unlabeled data for risk evaluation, i.e., label information is directly extracted from unlabeled data. In this paper, we extend PU classification to also incorporate negative data and propose a novel semi-supervised classification approach. We establish generalization error bounds for our novel methods and show that the bounds decrease with respect to the number of unlabeled data without the distributional assumptions that are required in existing semi-supervised classification methods. Through experiments, we demonstrate the usefulness of the proposed methods.

Includes members:
  Member "positive unlabeled learning with non negative risk estimator":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "object detection as a positive unlabeled problem")
  Member "theoretical comparisons of positive unlabeled learning against positive negative learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combating noisy labels by agreement a joint training method with co regularization")
  Member "theoretical comparisons of learning from positive negative positive unlabeled and negative unlabeled data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient training for positive unlabeled learning")
  Member "mixture proportion estimation via kernel embedding of distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards mixture proportion estimation without irreducibility")
  Member "pu learning for matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "advances in collaborative filtering and ranking")
  Member "convex and scalable weakly labeled svms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transfer adaptation learning a decade survey")
  Member "semi supervised learning of class balance under class prior change by distribution matching":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "domain adaptation with conditional distribution matching and generalized label shift")

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)