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: "efficient simulation of financial stress testing scenarios with suppes bayes causal networks"
  Abstract: The most recent financial upheavals have cast doubt on the adequacy of some of the conventional quantitative risk management strategies, such as VaR (Value at Risk), in many common situations. Consequently, there has been an increasing need for verisimilar financial stress testings, namely simulating and analyzing financial portfolios in extreme, albeit rare scenarios. Unlike conventional risk management which exploits statistical correlations among financial instruments, here we focus our analysis on the notion of probabilistic causation, which is embodied by Suppes-Bayes Causal Networks (SBCNs), SBCNs are probabilistic graphical models that have many attractive features in terms of more accurate causal analysis for generating financial stress scenarios. In this paper, we present a novel approach for conducting stress testing of financial portfolios based on SBCNs in combination with classical machine learning classification tools. The resulting method is shown to be capable of correctly discovering the causal relationships among financial factors that affect the portfolios and thus, simulating stress testing scenarios with a higher accuracy and lower computational complexity than conventional Monte Carlo Simulations.

Connected through hyperedge HE-1 (induced by source paper "probabilistic causal analysis of social influence"):
  with members:
    - "faster way to agony discovering hierarchies in directed graphs"
    - "exposing the probabilistic causal structure of discrimination"
    - "distinguishing between personal preferences and social influence in online activity feeds"
    - "a data based approach to social influence maximization"
    - "nectarss an rss feed ranking system that implicitly learns user preferences"

Connected through hyperedge HE-2 (induced by source paper "causal data science for financial stress testing"):
  with members:
    - "modeling cumulative biological phenomena with suppes bayes causal networks"
    - "efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena"
    - "exposing the probabilistic causal structure of discrimination"
    - "the temporal logic of causal structures"

Connected through hyperedge HE-3 (induced by source paper "efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena"):
  with members:
    - "causal data science for financial stress testing"
    - "modeling cumulative biological phenomena with suppes bayes causal networks"
    - "exposing the probabilistic causal structure of discrimination"
    - "parallel implementation of efficient search schemes for the inference of cancer progression models"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a bayesian method for constructing bayesian belief networks from databases"
    - "theory refinement on bayesian networks"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"

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)