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: "directed cyclic graphical representations of feedback models"
  Abstract: The use of directed acyclic graphs (DAGs) to represent conditional independence relations among random variables has proved fruitful in a variety of ways. Recursive structural equation models are one kind of DAG model. However, non-recursive structural equation models of the kinds used to model economic processes are naturally represented by directed cyclic graphs with independent errors, a characterization of conditional independence errors, a characterization of conditional independence constraints is obtained, and it is shown that the result generalizes in a natural way to systems in which the error variables or noises are statistically dependent. For non-linear systems with independent errors a sufficient condition for conditional independence of variables in associated distributions is obtained.

Connected through hyperedge HE-1 (induced by source paper "characterizing distribution equivalence for cyclic and acyclic directed graphs"):
  with members:
    - "a polynomial time algorithm for deciding markov equivalence of directed cyclic graphical models"
    - "a discovery algorithm for directed cyclis graphs"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "on the testable implications of causal models with hidden variables"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"
    - "discovering cyclic causal models by independent components analysis"

Connected through hyperedge HE-2 (induced by source paper "from random differential equations to structural causal models the stochastic case"):
  with members:
    - "from deterministic odes to dynamic structural causal models"
    - "learning why things change the difference based causality learner"
    - "discovering cyclic causal models by independent components analysis"

Connected through hyperedge HE-3 (induced by source paper "joint causal inference from multiple contexts"):
  with members:
    - "domain adaptation by using causal inference to predict invariant conditional distributions"
    - "ancestral causal inference"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "strong completeness and faithfulness in bayesian networks"
    - "learning sparse causal models is not np hard"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "causal discovery from changes"
    - "a general algorithm for deciding transportability of experimental results"

Connected through hyperedge HE-4 (induced by source paper "from deterministic odes to dynamic structural causal models"):
  with members:
    - "learning why things change the difference based causality learner"
    - "discovering cyclic causal models by independent components analysis"

Connected through hyperedge HE-5 (induced by source paper "enumerating cyclic orientations of a graph"):
  with members:
    - "a discovery algorithm for directed cyclis graphs"

Connected through hyperedge HE-6 (induced by source paper "reasoning about independence in probabilistic models of relational data"):
  with members:
    - "a factorization criterion for acyclic directed mixed graphs"
    - "on the logic of causal models"
    - "causal networks semantics and expressiveness"
    - "a sound and complete algorithm for learning causal models from relational data"
    - "causal inference in the presence of latent variables and selection bias"
    - "discriminative probabilistic models for relational data"
    - "a logical characterization of constraint based causal discovery"
    - "operations for learning with graphical models"

Connected through hyperedge HE-7 (induced by source paper "discovering cyclic causal models with latent variables a general sat based procedure"):
  with members:
    - "identifying independencies in causal graphs with feedback"
    - "bayesian networks from the point of view of chain graphs"
    - "incorporating causal prior knowledge as path constraints in bayesian networks and maximal ancestral graphs"
    - "on deducing conditional independence from d separation in causal graphs with feedback"

Connected through hyperedge HE-8 (induced by source paper "calculation of entailed rank constraints in partially non linear and cyclic models"):
  with members:
    - "causal inference in the presence of latent variables and selection bias"

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)