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 "adaptive sampling for coarse ranking"):
  Abstract: We consider the problem of active coarse ranking, where the goal is to sort items according to their means into clusters of pre-specified sizes, by adaptively sampling from their reward distributions. This setting is useful in many social science applications involving human raters and the approximate rank of every item is desired. Approximate or coarse ranking can significantly reduce the number of ratings required in comparison to the number needed to find an exact ranking. We propose a computationally efficient PAC algorithm LUCBRank for coarse ranking, and derive an upper bound on its sample complexity. We also derive a nearly matching distribution-dependent lower bound. Experiments on synthetic as well as real-world data show that LUCBRank performs better than state-of-the-art baseline methods, even when these methods have the advantage of knowing the underlying parametric model.

Includes members:
  Member "maximum selection and ranking under noisy comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ranking a set of objects a graph based least square approach")
  Member "practical algorithms for best k identification in multi armed bandits":
    also connected through other hyperedges:
      (none)
  Member "active ranking from pairwise comparisons and when parametric assumptions don t help":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ranking a set of objects a graph based least square approach")
  Member "deep learning the city quantifying urban perception at a global scale":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "facelift a transparent deep learning framework to beautify urban scenes")
  Member "rank centrality ranking from pair wise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "data driven rank breaking for efficient rank aggregation")
  Member "regret analysis of stochastic and nonstochastic multi armed bandit problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "active ranking using pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning from noisy similar and dissimilar data")
  Member "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "can an algorithm be my healthcare proxy")

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)