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: "thompson sampling for the mnl bandit"
  Abstract: We consider a sequential subset selection problem under parameter uncertainty, where at each time step, the decision maker selects a subset of cardinality $K$ from $N$ possible items (arms), and observes a (bandit) feedback in the form of the index of one of the items in said subset, or none. Each item in the index set is ascribed a certain value (reward), and the feedback is governed by a Multinomial Logit (MNL) choice model whose parameters are a priori unknown. The objective of the decision maker is to maximize the expected cumulative rewards over a finite horizon $T$, or alternatively, minimize the regret relative to an oracle that knows the MNL parameters. We refer to this as the MNL-Bandit problem. This problem is representative of a larger family of exploration-exploitation problems that involve a combinatorial objective, and arise in several important application domains. We present an approach to adapt Thompson Sampling to this problem and show that it achieves near-optimal regret as well as attractive numerical performance.

Connected through hyperedge HE-1 (induced by source paper "delay adaptive learning in generalized linear contextual bandits"):
  with members:
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "delay and cooperation in nonstochastic bandits"
    - "asymptotic convergence in online learning with unbounded delays"
    - "off policy evaluation for slate recommendation"
    - "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"

Connected through hyperedge HE-2 (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"
    - "mnl bandit a dynamic learning approach to assortment selection"
    - "multi dueling bandits with dependent arms"
    - "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-3 (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"
    - "mnl bandit a dynamic learning approach to assortment selection"
    - "a tutorial on thompson sampling"
    - "combinatorial multi armed bandit with general reward functions"
    - "cascading bandits learning to rank in the cascade model"

Connected through hyperedge HE-4 (induced by source paper "preselection bandits under the plackett luce model"):
  with members:
    - "combinatorial bandits with relative feedback"
    - "regret minimisation in multinomial logit bandits"
    - "preference based online learning with dueling bandits a survey"
    - "automated algorithm selection survey and perspectives"
    - "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"
    - "double thompson sampling for dueling bandits"

Connected through hyperedge HE-5 (induced by source paper "thompson sampling for contextual bandit problems with auxiliary safety constraints"):
  with members:
    - "linear stochastic bandits under safety constraints"
    - "deep kernel learning"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (induced by source paper "dynamic learning of sequential choice bandit problem under marketing fatigue"):
  with members:
    - "cascading bandits learning to rank in the cascade model"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-7 (induced by source paper "dynamic learning with frequent new product launches a sequential multinomial logit bandit problem"):
  with members:
    - "mnl bandit a dynamic learning approach to assortment selection"
    - "assortment optimization under the sequential multinomial logit model"
    - "linearly parameterized bandits"

Connected through hyperedge HE-8 (induced by source paper "thompson sampling for cascading bandits"):
  with members:
    - "thompson sampling for combinatorial semi bandits"
    - "thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "a tutorial on thompson sampling"
    - "online learning to rank in stochastic click models"
    - "multiple play bandits in the position based model"
    - "dcm bandits learning to rank with multiple clicks"
    - "cascading bandits for large scale recommendation problems"

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)