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 "diameter based interactive structure discovery"):
  Abstract: We introduce interactive structure discovery, a generic framework that encompasses many interactive learning settings, including active learning, top-k item identification, interactive drug discovery, and others. We adapt a recently developed active learning algorithm of Tosh and Dasgupta (2017) for interactive structure discovery, and show that the new algorithm can be made noise-tolerant and enjoys favorable query complexity bounds.

Includes members:
  Member "diameter based active learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive region based active learning")
  Member "a general framework for robust interactive learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the complexity of interactively learning a stable matching by trial and error")
  Member "interactive bayesian hierarchical clustering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bisect and conquer hierarchical clustering via max uncut bisection")
  Member "equality of opportunity in supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithmic fairness")
  Member "clustering with same cluster queries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explainable k means and k medians clustering")
  Member "efficient learning of linear separators under bounded noise":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "simple robust and optimal ranking from pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an introductory guide to fano s inequality with applications in statistical estimation")
  Member "local algorithms for interactive clustering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "semi bandit optimization in the dispersed setting")

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)