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: "on deducing conditional independence from d separation in causal graphs with feedback"
  Abstract: Pearl and Dechter (1996) claimed that the d-separation criterion for conditional independence in acyclic causal networks also applies to networks of discrete variables that have feedback cycles, provided that the variables of the system are uniquely determined by the random disturbances. I show by example that this is not true in general. Some condition stronger than uniqueness is needed, such as the existence of a causal dynamics guaranteed to lead to the unique solution.

Connected through hyperedge HE-1 (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-2 (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"
    - "directed cyclic graphical representations of feedback models"
    - "incorporating causal prior knowledge as path constraints in bayesian networks and maximal ancestral graphs"

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)