human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "assortment optimisation under a general discrete choice model a tight analysis of revenue ordered assortments"
  Abstract: The assortment problem in revenue management is the problem of deciding which subset of products to offer to consumers in order to maximise revenue. A simple and natural strategy is to select the best assortment out of all those that are constructed by fixing a threshold revenue π and then choosing all products with revenue at least π. This is known as the revenue-ordered assortments strategy. Our first contribution is an analysis of the performance of the revenue-ordered assortments strategy making only minimal assumptions about the underlying discrete choice model: We assume that consumers behave rationally, in the sense that the probability of choosing a specific product x ∈ S when given a choice set S cannot increase if S is enlarged. This rationality assumption, known as regularity, is satisfied by almost all models studied in the revenue management and choice theory literature. This includes in particular all random utility models, as well as other models introduced recently such as the additive perturbed utility model, the hitting fuzzy attention model, and models obtained using a non-additive random utility function. We provide three types of revenue guarantees for revenue-ordered assortments: If there are k distinct revenues r1, r2, ..., rk associated with the products (listed in increasing order), then revenue-ordered assortments approximate the optimum revenue to within a factor of (A) 1/k; (B) 1/(1 + ln(rk/r1)), and (C) 1/(1 + ln υ), where υ is defined with respect to an optimal assortment S* as the ratio between the probability of just buying a product and that of buying a product with highest revenue in S*. These three guarantees are in general incomparable, that is, (A), (B), or (C) can be the largest depending on the instance. We also show that the three bounds (A), (B), and (C) are exactly tight, in the sense that none of the bounds remains true if multiplied by a factor (1+ e) for any e > 0.   When applied to the special case of Mixed MNL models, bound (B) improves the recent analysis of revenue-ordered assortments by Rusmevichientong et al.(POMS, 2014), who showed a bound of 1/(e(1 + ln(rk/r1))).   Our second contribution is to draw a connection between assortment optimisation and some pricing problems studied in the theoretical computer science literature by showing that these pricing problems can be restated as an assortment problem under a discrete choice model satisfying the above-mentioned rationality assumption. This includes unit demand envy-free pricing problems and the Stackelberg minimum spanning tree problem. Building on that connection, we then observe that a well-studied heuristic in that area called uniform pricing corresponds in fact to the revenue-ordered assortment strategy for the specifically constructed discrete choice models. As a consequence, our revenue guarantees for revenue-ordered assortments apply. Interestingly, the resulting bounds match and unify known results on uniform pricing that were proved separately in the literature for the envy-free pricing problems and the Stackelberg minimum spanning tree problem.   This paper is available at https://arxiv.org/abs/1606.01371

Connected through hyperedge HE-1 (induced by source paper "active ranking with subset wise preferences"):
  with members:
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "data driven rank breaking for efficient rank aggregation"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "contextual dueling bandits"
    - "random utility theory for social choice"
    - "active ranking using pairwise comparisons"
    - "rank aggregation via nuclear norm minimization"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"

Connected through hyperedge HE-2 (induced by source paper "assortment and price optimization under the two stage luce model"):
  with members:
    - "maximum weighted independent sets with a budget"
    - "a variant of the maximum weight independent set problem"

Connected through hyperedge HE-3 (induced by source paper "assortment optimization under the sequential multinomial logit model"):
  with members:
    - "assortment optimization under a multinomial logit model with position bias and social influence"

Connected through hyperedge HE-4 (induced by source paper "assortment optimization under a multinomial logit model with position bias and social influence"):
  with members:
    - "measuring and optimizing cultural markets"

Question: Please tell me which class the center node 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)