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 "beyond disagreement based agnostic active learning"):
  Abstract: We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no assumptions on the true function generating the labels. The main algorithms for this problem are {\em{disagreement-based active learning}}, which has a high label requirement, and {\em{margin-based active learning}}, which only applies to fairly restricted settings. A major challenge is to find an algorithm which achieves better label complexity, is consistent in an agnostic setting, and applies to general classification problems. #R##N#In this paper, we provide such an algorithm. Our solution is based on two novel contributions -- a reduction from consistent active learning to confidence-rated prediction with guaranteed error, and a novel confidence-rated predictor.

Includes members:
  Member "the power of localization for efficiently learning linear separators with noise":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "noisy bayesian active learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning in confusion batch active learning with noisy oracle")
  Member "active and passive learning of linear separators under log concave distributions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive region based active learning")
  Member "agnostic active learning without constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "importance weighted active learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive region based active learning")
  Member "a tutorial on conformal prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ml aqp query driven approximate query processing based on machine learning")

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)