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: "a discovery algorithm for directed cyclis graphs"
  Abstract: Directed acyclic graphs have been used fruitfully to represent causal strucures (Pearl 1988). However, in the social sciences and elsewhere models are often used which correspond both causally and statistically to directed graphs with directed cycles (Spirtes 1995). Pearl (1993) discussed predicting the effects of intervention in models of this kind, so-called linear non-recursive structural equation models. This raises the question of whether it is possible to make inferences about causal structure with cycles, form sample data. In particular do there exist general, informative, feasible and reliable precedures for inferring causal structure from conditional independence relations among variables in a sample generated by an unknown causal structure? In this paper I present a discovery algorithm that is correct in the large sample limit, given commonly (but often implicitly) made plausible assumptions, and which provides information about the existence or non-existence of causal pathways from one variable to another. The algorithm is polynomial on sparse graphs.

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"
    - "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"
    - "discovering cyclic causal models by independent components analysis"

Connected through hyperedge HE-2 (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"
    - "discovering cyclic causal models by independent components analysis"

Connected through hyperedge HE-3 (induced by source paper "enumerating cyclic orientations of a graph"):
  with members:
    - "directed cyclic graphical representations of feedback models"

Connected through hyperedge HE-4 (induced by source paper "strong faithfulness and uniform consistency in causal inference"):
  with members:
    - "strong completeness and faithfulness in bayesian networks"
    - "learning bayesian networks the combination of knowledge and statistical data"

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)