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 "efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena"):
  Abstract: Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is further complicated by many theoretical issues, such as the I-equivalence among different structures. In this work, we focus on a specific subclass of BNs, named Suppes-Bayes Causal Networks (SBCNs), which include specific structural constraints based on Suppes' probabilistic causation to efficiently model cumulative phenomena. Here we compare the performance, via extensive simulations, of various state-of-the-art search strategies, such as local search techniques and Genetic Algorithms, as well as of distinct regularization methods. The assessment is performed on a large number of simulated datasets from topologies with distinct levels of complexity, various sample size and different rates of errors in the data. Among the main results, we show that the introduction of Suppes' constraints dramatically improve the inference accuracy, by reducing the solution space and providing a temporal ordering on the variables. We also report on trade-offs among different search techniques that can be efficiently employed in distinct experimental settings. This manuscript is an extended version of the paper "Structural Learning of Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018 International Conference on Computational Science.

Includes members:
  Member "causal data science for financial stress testing":
    also connected through other hyperedges:
      (none)
  Member "modeling cumulative biological phenomena with suppes bayes causal networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal data science for financial stress testing")
  Member "efficient simulation of financial stress testing scenarios with suppes bayes causal networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic causal analysis of social influence")
  Member "exposing the probabilistic causal structure of discrimination":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding agent incentives using causal influence diagrams part i single action settings")
  Member "parallel implementation of efficient search schemes for the inference of cancer progression models":
    also connected through other hyperedges:
      (none)
  Member "learning bayesian networks the combination of knowledge and statistical data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "a bayesian method for constructing bayesian belief networks from databases":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "needmining identifying micro blog data containing customer needs")
  Member "theory refinement on bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a tutorial on learning with bayesian networks")

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.LG(Machine Learning)