human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "accelerated learning from recommender systems using multi armed bandit"):
  Abstract: Recommendation systems are a vital component of many online marketplaces, where there are often millions of items to potentially present to users who have a wide variety of wants or needs. Evaluating recommender system algorithms is a hard task, given all the inherent bias in the data, and successful companies must be able to rapidly iterate on their solution to maintain their competitive advantage. The gold standard for evaluating recommendation algorithms has been the A/B test since it is an unbiased way to estimate how well one or more algorithms compare in the real world. However, there are a number of issues with A/B testing that make it impractical to be the sole method of testing, including long lead time, and high cost of exploration. We argue that multi armed bandit (MAB) testing as a solution to these issues. We showcase how we implemented a MAB solution as an extra step between offline and online A/B testing in a production system. We present the result of our experiment and compare all the offline, MAB, and online A/B tests metrics for our use case.

Includes members:
  Member "session based recommendation with graph neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "productization challenges of contextual multi armed bandits":
    also connected through other hyperedges:
      (none)
  Member "perturbed history exploration in stochastic multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "randomized exploration in generalized linear bandits")
  Member "on the difficulty of evaluating baselines a study on recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mixed dimension embeddings with application to memory efficient recommendation systems")
  Member "a batched multi armed bandit approach to news headline testing":
    also connected through other hyperedges:
      (none)
  Member "a simple deep personalized recommendation system":
    also connected through other hyperedges:
      (none)
  Member "a survey on session based recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "session based recommender systems for action selection in gui test generation")
  Member "multiplayer bandits without observing collision information":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "selfish robustness and equilibria in multi player bandits")

Question: Please tell me which class the source paper 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.IR(Information Retrieval)