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: "from observational studies to causal rule mining"
  Abstract: Randomised controlled trials (RCTs) are the most effective approach to causal discovery, but in many circumstances it is impossible to conduct RCTs. Therefore, observational studies based on passively observed data are widely accepted as an alternative to RCTs. However, in observational studies, prior knowledge is required to generate the hypotheses about the cause-effect relationships to be tested, and hence they can only be applied to problems with available domain knowledge and a handful of variables. In practice, many datasets are of high dimensionality, which leaves observational studies out of the opportunities for causal discovery from such a wealth of data sources. In another direction, many efficient data mining methods have been developed to identify associations among variables in large datasets. The problem is that causal relationships imply associations, but the reverse is not always true. However, we can see the synergy between the two paradigms here. Specifically, association rule mining can be used to deal with the high-dimensionality problem, whereas observational studies can be utilised to eliminate noncausal associations. In this article, we propose the concept of causal rules (CRs) and develop an algorithm for mining CRs in large datasets. We use the idea of retrospective cohort studies to detect CRs based on the results of association rule mining. Experiments with both synthetic and real-world datasets have demonstrated the effectiveness and efficiency of CR mining. In comparison with the commonly used causal discovery methods, the proposed approach generally is faster and has better or competitive performance in finding correct or sensible causes. It is also capable of finding a cause consisting of multiple variables—a feature that other causal discovery methods do not possess.

Connected through hyperedge HE-1 (induced by source paper "dependency aware software requirements selection using fuzzy graphs and integer programming"):
  with members:
    - "a study on the prevalence of human values in software engineering publications 2015 2018"
    - "solving the large scale next release problem with a backbone based multilevel algorithm"
    - "a hybrid aco algorithm for the next release problem"
    - "graded causation and defaults"

Connected through hyperedge HE-2 (induced by source paper "efficient local causal discovery based on markov blanket"):
  with members:
    - "causal discovery and forecasting in nonstationary environments with state space models"
    - "dag gnn dag structure learning with graph neural networks"
    - "causal inference and causal explanation with background knowledge"
    - "local structure discovery in bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "a fast pc algorithm for high dimensional causal discovery with multi core pcs"):
  with members:
    - "a parallel algorithm for exact bayesian structure discovery in bayesian networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a hybrid anytime algorithm for the constructiion of causal models from sparse data"
    - "adjacency faithfulness and conservative causal inference"

Connected through hyperedge HE-4 (induced by source paper "ubiquitous event mining to enhance personal health"):
  with members:
    - "cross modal health state estimation"

Connected through hyperedge HE-5 (induced by source paper "the crossover process learnability meets protection from inference attacks"):
  with members:
    - "loss factorization weakly supervised learning and label noise robustness"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "adjustment criteria in causal diagrams an algorithmic perspective"
    - "sharp analysis of low rank kernel matrix approximations"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-6 (induced by source paper "a review on algorithms for constraint based causal discovery"):
  with members:
    - "a fast pc algorithm for high dimensional causal discovery with multi core pcs"
    - "parallelpc an r package for efficient constraint based causal exploration"
    - "discovering cyclic causal models with latent variables a general sat based procedure"
    - "causal inference and causal explanation with background knowledge"
    - "incorporating causal prior knowledge as path constraints in bayesian networks and maximal ancestral graphs"
    - "adjacency faithfulness and conservative causal inference"
    - "on the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables"

Connected through hyperedge HE-7 (induced by source paper "the crossover process learnability and data protection from inference attacks"):
  with members:
    - "loss factorization weakly supervised learning and label noise robustness"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-8 (induced by source paper "causal inference in observational data"):
  with members:
    - "causal decision trees"
    - "a bayesian approach to learning causal networks"
    - "a theoretical study of y structures for causal discovery"

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)