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 "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"):
  Abstract: We study the active learning problem of top-$k$ ranking from multi-wise comparisons under the popular multinomial logit model. Our goal is to identify the top-$k$ items with high probability by adaptively querying sets for comparisons and observing the noisy output of the most preferred item from each comparison. To achieve this goal, we design a new active ranking algorithm without using any information about the underlying items' preference scores. We also establish a matching lower bound on the sample complexity even when the set of preference scores is given to the algorithm. These two results together show that the proposed algorithm is nearly instance optimal (similar to instance optimal [FLN03], but up to polylog factors). Our work extends the existing literature on rank aggregation in three directions. First, instead of studying a static problem with fixed data, we investigate the top-$k$ ranking problem in an active learning setting. Second, we show our algorithm is nearly instance optimal, which is a much stronger theoretical guarantee. Finally, we extend the pairwise comparison to the multi-wise comparison, which has not been fully explored in ranking literature.

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 "competitive analysis of the top k ranking problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "best item learning in random utility models with subset choices")
  Member "top k ranking from pairwise comparisons when spectral ranking is optimal":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ranking a set of objects a graph based least square approach")
  Member "parallel algorithms for select and partition with noisy comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "locally private hypothesis selection")
  Member "adversarial top k ranking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sorted top k in rounds")
  Member "spectral mle top k rank aggregation from pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ranking a set of objects a graph based least square approach")
  Member "simple robust and optimal ranking from pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "diameter based interactive structure discovery")
  Member "active ranking using pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning from noisy similar and dissimilar data")

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.DS(Data Structures and Algorithms)