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: "multiple play bandits in the position based model"
  Abstract: Sequentially learning to place items in multi-position displays or lists is a task that can be cast into the multiple-play semi-bandit setting. However, a major concern in this context is when the system cannot decide whether the user feedback for each item is actually exploitable. Indeed, much of the content may have been simply ignored by the user. The present work proposes to exploit available information regarding the display position bias under the so-called Position-based click model (PBM). We first discuss how this model differs from the Cascade model and its variants considered in several recent works on multiple-play bandits. We then provide a novel regret lower bound for this model as well as computationally efficient algorithms that display good empirical and theoretical performance.

Connected through hyperedge HE-1 (induced by source paper "unbiased learning to rank counterfactual and online approaches"):
  with members:
    - "optimizing ranking models in an online setting"
    - "to model or to intervene a comparison of counterfactual and online learning to rank from user interactions"
    - "differentiable unbiased online learning to rank"
    - "unbiased learning to rank with unbiased propensity estimation"
    - "consistent position bias estimation without online interventions for learning to rank"
    - "balancing speed and quality in online learning to rank for information retrieval"
    - "unbiased learning to rank with biased feedback"
    - "dcm bandits learning to rank with multiple clicks"

Connected through hyperedge HE-2 (induced by source paper "polynomial time algorithms for multiple arm identification with full bandit feedback"):
  with members:
    - "pac identification of many good arms in stochastic multi armed bandits"
    - "nearly optimal sampling algorithms for combinatorial pure exploration"
    - "pure exploration of multi armed bandit under matroid constraints"
    - "approximation of the quadratic knapsack problem"
    - "on the optimal sample complexity for best arm identification"
    - "powers of tensors and fast matrix multiplication"
    - "best arm identification in linear bandits"
    - "multiple identifications in multi armed bandits"

Connected through hyperedge HE-3 (induced by source paper "cascading non stationary bandits online learning to rank in the non stationary cascade model"):
  with members:
    - "merge double thompson sampling for large scale online ranker evaluation"
    - "online learning to rank in stochastic click models"
    - "dcm bandits learning to rank with multiple clicks"
    - "cascading bandits learning to rank in the cascade model"
    - "matroid bandits fast combinatorial optimization with learning"

Connected through hyperedge HE-4 (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"
    - "thompson sampling for the mnl bandit"
    - "dcm bandits learning to rank with multiple clicks"
    - "cascading bandits for large scale recommendation problems"

Connected through hyperedge HE-5 (induced by source paper "thompson sampling algorithms 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"
    - "thompson sampling for the mnl bandit"
    - "dcm bandits learning to rank with multiple clicks"
    - "cascading bandits for large scale recommendation problems"
    - "combinatorial cascading bandits"
    - "combinatorial bandits revisited"

Connected through hyperedge HE-6 (induced by source paper "reinforcement learning to rank in e commerce search engine formalization analysis and application"):
  with members:
    - "online learning to rank in stochastic click models"
    - "stochastic rank 1 bandits"
    - "cascading bandits for large scale recommendation problems"
    - "combinatorial cascading bandits"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "cascading bandits learning to rank in the cascade model"

Connected through hyperedge HE-7 (induced by source paper "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"):
  with members:
    - "combinatorial multi armed bandit with general reward functions"
    - "cascading bandits learning to rank in the cascade model"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "matroid bandits fast combinatorial optimization with learning"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-8 (induced by source paper "online learning to rank in stochastic click models"):
  with members:
    - "bernoulli rank 1 bandits for click feedback"
    - "stochastic rank 1 bandits"
    - "dcm bandits learning to rank with multiple clicks"
    - "cascading bandits for large scale recommendation problems"
    - "cascading bandits learning to rank in the cascade model"

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)