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: "incorporating causal prior knowledge as path constraints in bayesian networks and maximal ancestral graphs"
  Abstract: We consider the incorporation of causal knowledge about the presence or absence of (possibly indirect) causal relations into a causal model. Such causal relations correspond to directed paths in a causal model. This type of knowledge naturally arises from experimental data, among others. Specifically, we consider the formalisms of Causal Bayesian Networks and Maximal Ancestral Graphs and their Markov equivalence classes: Partially Directed Acyclic Graphs and Partially Oriented Ancestral Graphs. We introduce sound and complete procedures which are able to incorporate causal prior knowledge in such models. In simulated experiments, we show that often considering even a few causal facts leads to a significant number of new inferences. In a case study, we also show how to use real experimental data to infer causal knowledge and incorporate it into a real biological causal network. The code is available at mensxmachina.org.

Connected through hyperedge HE-1 (induced by source paper "a review on algorithms for constraint based causal discovery"):
  with members:
    - "a fast pc algorithm for high dimensional causal discovery with multi core pcs"
    - "from observational studies to causal rule mining"
    - "parallelpc an r package for efficient constraint based causal exploration"
    - "discovering cyclic causal models with latent variables a general sat based procedure"
    - "causal inference and causal explanation with background knowledge"
    - "adjacency faithfulness and conservative causal inference"
    - "on the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables"

Connected through hyperedge HE-2 (induced by source paper "ancestral causal inference"):
  with members:
    - "clingo asp control preliminary report"
    - "causal discovery from changes"
    - "cyclic causal discovery from continuous equilibrium data"
    - "a logical characterization of constraint based causal discovery"
    - "adjacency faithfulness and conservative causal inference"
    - "a bayesian approach to constraint based causal inference"

Connected through hyperedge HE-3 (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"
    - "on deducing conditional independence from d separation in causal graphs with feedback"

Connected through hyperedge HE-4 (induced by source paper "scoring and searching over bayesian networks with causal and associative priors"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "causal inference and causal explanation with background knowledge"
    - "theory refinement on bayesian networks"
    - "mantaining dynamic matrices for fully dynamic transitive closure"

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)