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: "causal rule sets for identifying subgroups with enhanced treatment effect"
  Abstract: We introduce a novel generative model for interpretable subgroup analysis for causal inference applications, Causal Rule Sets (CRS). A CRS model uses a small set of short rules to capture a subgroup where the average treatment effect is elevated compared to the entire population. We present a Bayesian framework for learning a causal rule set. The Bayesian framework consists of a prior that favors simpler models and a Bayesian logistic regression that characterizes the relation between outcomes, attributes and subgroup membership. We find maximum a posteriori models using discrete Monte Carlo steps in the joint solution space of rules sets and parameters. We provide theoretically grounded heuristics and bounding strategies to improve search efficiency. Experiments show that the search algorithm can efficiently recover a true underlying subgroup and CRS shows consistently competitive performance compared to other state-of-the-art baseline methods.

Connected through hyperedge HE-1 (induced by source paper "interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines"):
  with members:
    - "bayesian inference of individualized treatment effects using multi task gaussian processes"
    - "machine learning for drug overdose surveillance"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "prophit causal inverse classification for multiple continuously valued treatment policies"):
  with members:
    - "bayesian inference of individualized treatment effects using multi task gaussian processes"
    - "recommendations as treatments debiasing learning and evaluation"
    - "counterfactual risk minimization learning from logged bandit feedback"

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)