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: "graphical models and exponential families"
  Abstract: We provide a classification of graphical models according to their representation as subfamilies of exponential families. Undirected graphical models with no hidden variables are linear exponential families (LEFs), directed acyclic graphical models and chain graphs with no hidden variables, including Bayesian networks with several families of local distributions, are curved exponential families (CEFs) and graphical models with hidden variables are stratified exponential families (SEFs). An SEF is a finite union of CEFs satisfying a frontier condition. In addition, we illustrate how one can automatically generate independence and non-independence constraints on the distributions over the observable variables implied by a Bayesian network with hidden variables. The relevance of these results for model selection is examined.

Connected through hyperedge HE-1 (induced by source paper "instrumentality tests revisited"):
  with members:
    - "causality in bayesian belief networks"
    - "on the testability of causal models with latent and instrumental variables"
    - "decision theoretic foundations for causal reasoning"

Connected through hyperedge HE-2 (induced by source paper "quantifier elimination for statistical problems"):
  with members:
    - "counterfactual probabilities computational methods bounds and applications"

Connected through hyperedge HE-3 (induced by source paper "inequality constraints in causal models with hidden variables"):
  with members:
    - "counterfactual probabilities computational methods bounds and applications"
    - "quantifier elimination for statistical problems"
    - "on the testability of causal models with latent and instrumental variables"
    - "causal discovery from a mixture of experimental and observational data"
    - "instrumentality tests revisited"
    - "a bayesian approach to learning causal networks"
    - "on the testable implications of causal models with hidden variables"
    - "decision theoretic foundations for causal reasoning"

Connected through hyperedge HE-4 (induced by source paper "algebraic statistics in model selection"):
  with members:
    - "on the geometry of bayesian graphical models with hidden variables"
    - "dimension correction for hierarchical latent class models"
    - "automated analytic asymptotic evaluation of the marginal likelihood for latent models"
    - "asymptotic model selection for naive bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "polynomial constraints in causal bayesian networks"):
  with members:
    - "inequality constraints in causal models with hidden variables"
    - "on the testable implications of causal models with hidden variables"
    - "decision theoretic foundations for causal reasoning"

Connected through hyperedge HE-6 (induced by source paper "on the testable implications of causal models with hidden variables"):
  with members:
    - "on the testability of causal models with latent and instrumental variables"

Connected through hyperedge HE-7 (induced by source paper "a sequence of relaxations constraining hidden variable models"):
  with members:
    - "quantifier elimination for statistical problems"
    - "instrumentality tests revisited"
    - "inequality constraints in causal models with hidden variables"
    - "polynomial constraints in causal bayesian networks"
    - "on the testable implications of causal models with hidden variables"
    - "information contagion an empirical study of the spread of news on digg and twitter social 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)