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: "cascading bandits for large scale recommendation problems"
  Abstract: Most recommender systems recommend a list of items. The user examines the list, from the first item to the last, and often chooses the first attractive item and does not examine the rest. This type of user behavior can be modeled by the cascade model. In this work, we study cascading bandits, an online learning variant of the cascade model where the goal is to recommend $K$ most attractive items from a large set of $L$ candidate items. We propose two algorithms for solving this problem, which are based on the idea of linear generalization. The key idea in our solutions is that we learn a predictor of the attraction probabilities of items from their features, as opposing to learning the attraction probability of each item independently as in the existing work. This results in practical learning algorithms whose regret does not depend on the number of items $L$. We bound the regret of one algorithm and comprehensively evaluate the other on a range of recommendation problems. The algorithm performs well and outperforms all baselines.

Connected through hyperedge HE-1 (induced by source paper "best arm identification for cascading bandits in the fixed confidence setting"):
  with members:
    - "combinatorial bandits with full bandit feedback sample complexity and regret minimization"
    - "thompson sampling for cascading bandits"
    - "thompson sampling algorithms for cascading bandits"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "combinatorial cascading bandits"
    - "matroid bandits fast combinatorial optimization with learning"
    - "a variant of azuma s inequality for martingales with subgaussian tails"

Connected through hyperedge HE-2 (induced by source paper "bounded regret for finitely parameterized multi armed bandits"):
  with members:
    - "bounded regret for finite armed structured bandits"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "leveraging side observations in stochastic bandits"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "bandit problems with side observations"

Connected through hyperedge HE-3 (induced by source paper "observe before play multi armed bandit with pre observations"):
  with members:
    - "cost aware cascading bandits"
    - "multi armed bandit learning in iot networks learning helps even in non stationary settings"
    - "multi user lax communications a multi armed bandit approach"
    - "multi player bandits a musical chairs approach"
    - "combinatorial cascading bandits"
    - "cascading bandits learning to rank in the cascade model"
    - "concurrent bandits and cognitive radio networks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-4 (induced by source paper "a contextual bandit approach to online learning to rank for relevance and diversity"):
  with members:
    - "cascading non stationary bandits online learning to rank in the non stationary cascade model"
    - "to model or to intervene a comparison of counterfactual and online learning to rank from user interactions"
    - "bubblerank safe online learning to re rank via implicit click feedback"
    - "merge double thompson sampling for large scale online ranker evaluation"
    - "ranking for relevance and display preferences in complex presentation layouts"
    - "differentiable unbiased online learning to rank"
    - "online learning to rank in stochastic click models"
    - "dcm bandits learning to rank with multiple clicks"

Connected through hyperedge HE-5 (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"
    - "multiple play bandits in the position based model"
    - "dcm bandits learning to rank with multiple clicks"

Connected through hyperedge HE-6 (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"
    - "multiple play bandits in the position based model"
    - "dcm bandits learning to rank with multiple clicks"
    - "combinatorial cascading bandits"
    - "combinatorial bandits revisited"

Connected through hyperedge HE-7 (induced by source paper "online learning with feedback graphs and switching costs"):
  with members:
    - "unifying the stochastic and the adversarial bandits with knapsack"
    - "bandits with movement costs and adaptive pricing"
    - "multi armed bandits with metric movement costs"
    - "dcm bandits learning to rank with multiple clicks"
    - "on independent sets in random graphs"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "bandits with switching costs t 2 3 regret"

Connected through hyperedge HE-8 (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"
    - "multiple play bandits in the position based model"
    - "combinatorial cascading bandits"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "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)