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: "a batched multi armed bandit approach to news headline testing"
  Abstract: Optimizing news headlines is important for publishers and media sites. A compelling headline will increase readership, user engagement and social shares. At Yahoo Front Page, headline testing is carried out using a test-rollout strategy: we first allocate equal proportion of the traffic to each headline variation for a defined testing period, and then shift all future traffic to the best-performing variation. In this paper, we introduce a multi-armed bandit (MAB) approach with batched Thompson Sampling (bTS) to dynamically test headlines for news articles. This method is able to gradually allocate traffic towards optimal headlines while testing. We evaluate the bTS method based on empirical impressions/clicks data and simulated user responses. The result shows that the bTS method is robust, converges accurately and quickly to the optimal headline, and outperforms the test-rollout strategy by 3.69% in terms of clicks.

Connected through hyperedge HE-1 (induced by source paper "accelerated learning from recommender systems using multi armed bandit"):
  with members:
    - "session based recommendation with graph neural network"
    - "productization challenges of contextual multi armed bandits"
    - "perturbed history exploration in stochastic multi armed bandits"
    - "on the difficulty of evaluating baselines a study on recommender systems"
    - "a simple deep personalized recommendation system"
    - "a survey on session based recommender systems"
    - "multiplayer bandits without observing collision information"
    - "ucboost a boosting approach to tame complexity and optimality for stochastic bandits"

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)