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 semi supervised and inductive embedding model for churn prediction of large scale mobile games"
  Abstract: Mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have been developed around the world. One critical challenge for these platforms and services is to understand user churn behavior in mobile games. Accurate churn prediction will benefit many stakeholders such as game developers, advertisers, and platform operators. In this paper, we present the first large-scale churn prediction solution for mobile games. In view of the common limitations of the state-of-the-art methods built upon traditional machine learning models, we devise a novel semi-supervised and inductive embedding model that jointly learns the prediction function and the embedding function for user-app relationships. We model these two functions by deep neural networks with a unique edge embedding technique that is able to capture both contextual information and relationship dynamics. We also design a novel attributed random walk technique that takes into consideration both topological adjacency and attribute similarities. To evaluate the performance of our solution, we collect real-world data from the Samsung Game Launcher platform that includes tens of thousands of games and hundreds of millions of user-app interactions. The experimental results with this data demonstrate the superiority of our proposed model against existing state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "developing multi task recommendations with long term rewards via policy distilled reinforcement learning"):
  with members:
    - "micro and macro level churn analysis of large scale mobile games"
    - "joint neural collaborative filtering for recommender systems"
    - "value aware recommendation based on reinforced profit maximization in e commerce systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation"

Connected through hyperedge HE-2 (induced by source paper "micro and macro level churn analysis of large scale mobile games"):
  with members:
    - "streaming network embedding through local actions"
    - "learning edge representations via low rank asymmetric projections"
    - "semi supervised embedding in attributed networks with outliers"
    - "customer lifetime value prediction using embeddings"
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "modelling early user game interactions for joint estimation of survival time and churn probability"):
  with members:
    - "cyclical learning rates for training neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "utilizing players in game time spending records for churn prediction mining in game time spending regularity"):
  with members:
    - "game data mining competition on churn prediction and survival analysis using commercial game log data"

Connected through hyperedge HE-5 (induced by source paper "utilizing players playtime records for churn prediction mining playtime regularity"):
  with members:
    - "game data mining competition on churn prediction and survival analysis using commercial game log data"

Connected through hyperedge HE-6 (induced by source paper "heteroscedastic bandits with reneging"):
  with members:
    - "thompson sampling for contextual bandits with linear payoffs"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "stay with me lifetime maximization through heteroscedastic linear bandits with reneging"):
  with members:
    - "a contextual bandit approach to personalized news article recommendation"

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)