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: "discovering cyclic causal models by independent components analysis"
  Abstract: We generalize Shimizu et al's (2006) ICA-based approach for discovering linear non-Gaussian acyclic (LiNGAM) Structural Equation Models (SEMs) from causally sufficient, continuous-valued observational data. By relaxing the assumption that the generating SEM's graph is acyclic, we solve the more general problem of linear non-Gaussian (LiNG) SEM discovery. LiNG discovery algorithms output the distribution equivalence class of SEMs which, in the large sample limit, represents the population distribution. We apply a LiNG discovery algorithm to simulated data. Finally, we give sufficient conditions under which only one of the SEMs in the output class is 'stable'.

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"
    - "directed cyclic graphical representations of feedback models"
    - "on the testable implications of causal models with hidden variables"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "causal constraints models"):
  with members:
    - "learning why things change the difference based causality learner"

Connected through hyperedge HE-3 (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"
    - "directed cyclic graphical representations of feedback models"
    - "learning why things change the difference based causality learner"

Connected through hyperedge HE-4 (induced by source paper "beyond structural causal models causal constraints models"):
  with members:
    - "from random differential equations to structural causal models the stochastic case"
    - "from deterministic odes to dynamic structural causal models"

Connected through hyperedge HE-5 (induced by source paper "identifiability and transportability in dynamic causal networks"):
  with members:
    - "a probabilistic calculus of actions"
    - "an efficient algorithm for computing interventional distributions in latent variable causal models"
    - "identifying conditional causal effects"
    - "learning why things change the difference based causality learner"

Connected through hyperedge HE-6 (induced by source paper "from deterministic odes to dynamic structural causal models"):
  with members:
    - "directed cyclic graphical representations of feedback models"
    - "learning why things change the difference based causality learner"

Connected through hyperedge HE-7 (induced by source paper "data driven root cause analysis for distributed system anomalies"):
  with members:
    - "an unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed cps"
    - "a discovery algorithm for directed cyclis graphs"

Connected through hyperedge HE-8 (induced by source paper "root cause analysis for time series anomalies via spatiotemporal causal graphical modeling"):
  with members:
    - "an unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed cps"

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)