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: "mnl bandit a dynamic learning approach to assortment selection"
  Abstract: We consider a dynamic assortment selection problem, where in every round the retailer offers a subset (assortment) of $N$ substitutable products to a consumer, who selects one of these products according to a multinomial logit (MNL) choice model. The retailer observes this choice and the objective is to dynamically learn the model parameters, while optimizing cumulative revenues over a selling horizon of length $T$. We refer to this exploration-exploitation formulation as the MNL-Bandit problem. Existing methods for this problem follow an "explore-then-exploit" approach, which estimate parameters to a desired accuracy and then, treating these estimates as if they are the correct parameter values, offers the optimal assortment based on these estimates. These approaches require certain a priori knowledge of "separability", determined by the true parameters of the underlying MNL model, and this in turn is critical in determining the length of the exploration period. (Separability refers to the distinguishability of the true optimal assortment from the other sub-optimal alternatives.) In this paper, we give an efficient algorithm that simultaneously explores and exploits, achieving performance independent of the underlying parameters. The algorithm can be implemented in a fully online manner, without knowledge of the horizon length $T$. Furthermore, the algorithm is adaptive in the sense that its performance is near-optimal in both the "well separated" case, as well as the general parameter setting where this separation need not hold.

Connected through hyperedge HE-1 (induced by source paper "combinatorial bandits with relative feedback"):
  with members:
    - "pac ranking from pairwise and listwise queries lower bounds and upper bounds"
    - "pac battling bandits in the plackett luce model"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "multi dueling bandits with dependent arms"
    - "thompson sampling for the mnl bandit"
    - "dcm bandits learning to rank with multiple clicks"
    - "multi dueling bandits and their application to online ranker evaluation"
    - "data driven rank breaking for efficient rank aggregation"

Connected through hyperedge HE-2 (induced by source paper "thompson sampling for a fatigue aware online recommendation system"):
  with members:
    - "dynamic learning of sequential choice bandit problem under marketing fatigue"
    - "thompson sampling for combinatorial semi bandits"
    - "thompson sampling for cascading bandits"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "a tutorial on thompson sampling"
    - "thompson sampling for the mnl bandit"
    - "combinatorial multi armed bandit with general reward functions"
    - "cascading bandits learning to rank in the cascade model"

Connected through hyperedge HE-3 (induced by source paper "dynamic learning with frequent new product launches a sequential multinomial logit bandit problem"):
  with members:
    - "assortment optimization under the sequential multinomial logit model"
    - "thompson sampling for the mnl bandit"
    - "linearly parameterized bandits"

Connected through hyperedge HE-4 (induced by source paper "game of thrones fully distributed learning for multi player bandits"):
  with members:
    - "distributed learning for channel allocation over a shared spectrum"
    - "coordinated exploration in concurrent reinforcement learning"
    - "the effect of communication on noncooperative multiplayer multi armed bandit problems"
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "distributed clustering of linear bandits in peer to peer networks"
    - "delay and cooperation in nonstochastic bandits"
    - "multi user lax communications a multi armed bandit approach"
    - "multi player bandits a musical chairs approach"

Connected through hyperedge HE-5 (induced by source paper "dynamic pricing under a static calendar"):
  with members:
    - "inventory balancing with online learning"
    - "on policies for single leg revenue management with limited demand information"
    - "combinatorial auctions via posted prices"
    - "matroid prophet inequalities"

Connected through hyperedge HE-6 (induced by source paper "multinomial logit bandit with linear utility functions"):
  with members:
    - "assortment optimization under unknown multinomial logit choice models"
    - "thompson sampling for the mnl bandit"
    - "combinatorial multi armed bandit with general reward functions"

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.LG(Machine Learning)