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 "active ranking with subset wise preferences"):
  Abstract: We consider the problem of probably approximately correct (PAC) ranking $n$ items by adaptively eliciting subset-wise preference feedback. At each round, the learner chooses a subset of $k$ items and observes stochastic feedback indicating preference information of the winner (most preferred) item of the chosen subset drawn according to a Plackett-Luce (PL) subset choice model unknown a priori. The objective is to identify an $\epsilon$-optimal ranking of the $n$ items with probability at least $1 - \delta$. When the feedback in each subset round is a single Plackett-Luce-sampled item, we show $(\epsilon, \delta)$-PAC algorithms with a sample complexity of $O\left(\frac{n}{\epsilon^2} \ln \frac{n}{\delta} \right)$ rounds, which we establish as being order-optimal by exhibiting a matching sample complexity lower bound of $\Omega\left(\frac{n}{\epsilon^2} \ln \frac{n}{\delta} \right)$---this shows that there is essentially no improvement possible from the pairwise comparisons setting ($k = 2$). When, however, it is possible to elicit top-$m$ ($\leq k$) ranking feedback according to the PL model from each adaptively chosen subset of size $k$, we show that an $(\epsilon, \delta)$-PAC ranking sample complexity of $O\left(\frac{n}{m \epsilon^2} \ln \frac{n}{\delta} \right)$ is achievable with explicit algorithms, which represents an $m$-wise reduction in sample complexity compared to the pairwise case. This again turns out to be order-wise unimprovable across the class of symmetric ranking algorithms. Our algorithms rely on a novel {pivot trick} to maintain only $n$ itemwise score estimates, unlike $O(n^2)$ pairwise score estimates that has been used in prior work. We report results of numerical experiments that corroborate our findings.

Includes members:
  Member "a nearly instance optimal algorithm for top k ranking under the multinomial logit model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "assortment optimisation under a general discrete choice model a tight analysis of revenue ordered assortments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "assortment and price optimization under the two stage luce model")
  Member "data driven rank breaking for efficient rank aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "spectral mle top k rank aggregation from pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ranking a set of objects a graph based least square approach")
  Member "contextual dueling bandits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online preselection with context information under the plackett luce model")
  Member "random utility theory for social choice":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "active ranking using pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning from noisy similar and dissimilar data")
  Member "rank aggregation via nuclear norm minimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "weighted matrix completion from non random non uniform sampling patterns")

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)