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: "efficient learning in large scale combinatorial semi bandits"
  Abstract: A stochastic combinatorial semi-bandit is an online learning problem where at each step a learning agent chooses a subset of ground items subject to combinatorial constraints, and then observes stochastic weights of these items and receives their sum as a payoff. In this paper, we consider efficient learning in large-scale combinatorial semi-bandits with linear generalization, and as a solution, propose two learning algorithms called Combinatorial Linear Thompson Sampling (CombLinTS) and Combinatorial Linear UCB (CombLinUCB). Both algorithms are computationally efficient as long as the offline version of the combinatorial problem can be solved efficiently. We establish that CombLinTS and CombLinUCB are also provably statistically efficient under reasonable assumptions, by developing regret bounds that are independent of the problem scale (number of items) and sublinear in time. We also evaluate CombLinTS on a variety of problems with thousands of items. Our experiment results demonstrate that CombLinTS is scalable, robust to the choice of algorithm parameters, and significantly outperforms the best of our baselines.

Connected through hyperedge HE-1 (induced by source paper "advances in bandits with knapsacks"):
  with members:
    - "introduction to multi armed bandits"
    - "unifying the stochastic and the adversarial bandits with knapsack"
    - "adversarial bandits with knapsacks"
    - "combinatorial semi bandits with knapsacks"
    - "logarithmic regret bounds for bandits with knapsacks"
    - "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives"
    - "resourceful contextual bandits"
    - "bandits with knapsacks"

Connected through hyperedge HE-2 (induced by source paper "differentiable bandit exploration"):
  with members:
    - "meta learning of sequential strategies"
    - "empirical bayes regret minimization"
    - "boltzmann exploration done right"
    - "a simple neural attentive meta learner"
    - "algorithms for multi armed bandit problems"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"
    - "thompson sampling for contextual bandits with linear payoffs"

Connected through hyperedge HE-3 (induced by source paper "thompson sampling for combinatorial network optimization in unknown environments"):
  with members:
    - "batch size independent regret bounds for the combinatorial multi armed bandit problem"
    - "stochastic online learning with probabilistic graph feedback"
    - "combinatorial bandits with full bandit feedback sample complexity and regret minimization"
    - "thompson sampling for combinatorial semi bandits"
    - "bandits with side observations bounded vs logarithmic regret"
    - "analysis of thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms"
    - "regret bounds for stochastic combinatorial multi armed bandits with linear space complexity"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"

Connected through hyperedge HE-4 (induced by source paper "robust influence maximization for hyperparametric models"):
  with members:
    - "robust optimization for non convex objectives"
    - "model independent online learning for influence maximization"
    - "robust influence maximization"
    - "robust influence maximization"
    - "stability of influence maximization"
    - "analytical approach to parallel repetition"
    - "a data based approach to social influence maximization"

Connected through hyperedge HE-5 (induced by source paper "waterfall bandits learning to sell ads online"):
  with members:
    - "combinatorial cascading bandits"
    - "cascading bandits learning to rank in the cascade model"
    - "sequential posted price mechanisms with correlated valuations"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "dynamic pricing with limited supply"

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"
    - "cascading bandits for large scale recommendation problems"
    - "combinatorial cascading bandits"

Connected through hyperedge HE-7 (induced by source paper "thompson sampling for combinatorial semi bandits"):
  with members:
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "combinatorial multi armed bandit with general reward functions"
    - "combinatorial cascading bandits"
    - "combinatorial bandits revisited"
    - "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"
    - "an information theoretic analysis of thompson sampling"

Connected through hyperedge HE-8 (induced by source paper "offline evaluation of ranking policies with click models"):
  with members:
    - "unbiased learning to rank with biased feedback"
    - "off policy evaluation for slate recommendation"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "learning from logged implicit exploration data"

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)