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 "maximum selection and ranking under noisy comparisons"):
  Abstract: We consider $(\epsilon,\delta)$-PAC maximum-selection and ranking for general probabilistic models whose comparisons probabilities satisfy strong stochastic transitivity and stochastic triangle inequality. Modifying the popular knockout tournament, we propose a maximum-selection algorithm that uses $\mathcal{O}\left(\frac{n}{\epsilon^2}\log \frac{1}{\delta}\right)$ comparisons, a number tight up to a constant factor. We then derive a general framework that improves the performance of many ranking algorithms, and combine it with merge sort and binary search to obtain a ranking algorithm that uses $\mathcal{O}\left(\frac{n\log n (\log \log n)^3}{\epsilon^2}\right)$ comparisons for any $\delta\ge\frac1n$, a number optimal up to a $(\log \log n)^3$ factor.

Includes members:
  Member "active ranking from pairwise comparisons and when parametric assumptions don t help":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ranking a set of objects a graph based least square approach")
  Member "maximum selection and sorting with adversarial comparators and an application to density estimation":
    also connected through other hyperedges:
      (none)
  Member "top k ranking from pairwise comparisons when spectral ranking is optimal":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ranking a set of objects a graph based least square approach")
  Member "efficient algorithms for adversarial contextual learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "sorting from noisy information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "noise tolerant reliable active classification with comparison queries")

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)