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: "algorithms for multi armed bandit problems"
  Abstract: Although many algorithms for the multi-armed bandit problem are well-understood theoretically, empirical confirmation of their effectiveness is generally scarce. This paper presents a thorough empirical study of the most popular multi-armed bandit algorithms. Three important observations can be made from our results. Firstly, simple heuristics such as epsilon-greedy and Boltzmann exploration outperform theoretically sound algorithms on most settings by a significant margin. Secondly, the performance of most algorithms varies dramatically with the parameters of the bandit problem. Our study identifies for each algorithm the settings where it performs well, and the settings where it performs poorly. Thirdly, the algorithms' performance relative each to other is affected only by the number of bandit arms and the variance of the rewards. This finding may guide the design of subsequent empirical evaluations. In the second part of the paper, we turn our attention to an important area of application of bandit algorithms: clinical trials. Although the design of clinical trials has been one of the principal practical problems motivating research on multi-armed bandits, bandit algorithms have never been evaluated as potential treatment allocation strategies. Using data from a real study, we simulate the outcome that a 2001-2002 clinical trial would have had if bandit algorithms had been used to allocate patients to treatments. We find that an adaptive trial would have successfully treated at least 50% more patients, while significantly reducing the number of adverse effects and increasing patient retention. At the end of the trial, the best treatment could have still been identified with a high level of statistical confidence. Our findings demonstrate that bandit algorithms are attractive alternatives to current adaptive treatment allocation strategies.

Connected through hyperedge HE-1 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems"):
  with members:
    - "deep conversational recommender in travel"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "toward building conversational recommender systems a contextual bandit approach"
    - "towards deep conversational recommendations"
    - "learning contextual bandits in a non stationary environment"
    - "collaborative memory network for recommendation systems"
    - "explicit state tracking with semi supervisionfor neural dialogue generation"
    - "aspect aware latent factor model rating prediction with ratings and reviews"

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"
    - "efficient learning in large scale combinatorial semi bandits"
    - "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 "robust stochastic bandit algorithms under probabilistic unbounded adversarial attack"):
  with members:
    - "data poisoning attacks on stochastic bandits"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "stochastic bandits robust to adversarial corruptions"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "an algorithm with nearly optimal pseudo regret for both stochastic and adversarial bandits"
    - "the best of both worlds stochastic and adversarial bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "decentralized online learning algorithms for opportunistic spectrum access"

Connected through hyperedge HE-4 (induced by source paper "nonparametric pricing analytics with customer covariates"):
  with members:
    - "a primal dual learning algorithm for personalized dynamic pricing with an inventory constraint"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "x armed bandits"
    - "contextual bandits with similarity information"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-5 (induced by source paper "combining offline causal inference and online bandit learning for data driven decisions"):
  with members:
    - "the deconfounded recommender a causal inference approach to recommendation"
    - "nonparametric stochastic contextual bandits"
    - "counterfactual risk minimization learning from logged bandit feedback"
    - "doubly robust policy evaluation and learning"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (induced by source paper "empirical bayes regret minimization"):
  with members:
    - "randomized exploration in generalized linear bandits"
    - "learning to branch"
    - "probabilistic model agnostic meta learning"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "a simple neural attentive meta learner"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "collaborative filtering bandits"

Connected through hyperedge HE-7 (induced by source paper "learning orthogonal projections in linear bandits"):
  with members:
    - "task recommendation in crowdsourcing based on learning preferences and reliabilities"
    - "context aware hierarchical online learning for performance maximization in mobile crowdsourcing"
    - "sequential multi class labeling in crowdsourcing"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-8 (induced by source paper "rloc neurobiologically inspired hierarchical reinforcement learning algorithm for continuous control of nonlinear dynamical systems"):
  with members:
    - "data efficient reinforcement learning with probabilistic model predictive control"
    - "modular multitask reinforcement learning with policy sketches"
    - "benchmarking deep reinforcement learning for continuous control"
    - "end to end training of deep visuomotor policies"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

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.AI(Artificial Intelligence)