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 with latent variables a general sat based procedure"
  Abstract: We present a very general approach to learning the structure of causal models based on d-separation constraints, obtained from any given set of overlapping passive observational or experimental data sets. The procedure allows for both directed cycles (feedback loops) and the presence of latent variables. Our approach is based on a logical representation of causal pathways, which permits the integration of quite general background knowledge, and inference is performed using a Boolean satisfiability (SAT) solver. The procedure is complete in that it exhausts the available information on whether any given edge can be determined to be present or absent, and returns "unknown" otherwise. Many existing constraint-based causal discovery algorithms can be seen as special cases, tailored to circumstances in which one or more restricting assumptions apply. Simulations illustrate the effect of these assumptions on discovery and how the present algorithm scales.

Connected through hyperedge HE-1 (induced by source paper "towards robust relational causal discovery"):
  with members:
    - "ancestral causal inference"
    - "a hybrid anytime algorithm for the constructiion of causal models from sparse data"
    - "a sound and complete algorithm for learning causal models from relational data"
    - "causal inference in the presence of latent variables and selection bias"
    - "a kernel method for the two sample problem"

Connected through hyperedge HE-2 (induced by source paper "asp based discovery of semi markovian causal models under weaker assumptions"):
  with members:
    - "ancestral causal inference"
    - "on the testable implications of causal models with hidden variables"
    - "adjacency faithfulness and conservative causal inference"

Connected through hyperedge HE-3 (induced by source paper "using noisy extractions to discover causal knowledge"):
  with members:
    - "ancestral causal inference"

Connected through hyperedge HE-4 (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"
    - "causal inference and causal explanation with background knowledge"
    - "incorporating causal prior knowledge as path constraints in bayesian networks and maximal ancestral graphs"
    - "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-5 (induced by source paper "computing minimal sets on propositional formulae i problems reductions"):
  with members:
    - "automatic abstraction in smt based unbounded software model checking"
    - "cause clue clauses error localization using maximum satisfiability"
    - "redundancy in logic i cnf propositional formulae"
    - "redundancy in logic ii 2cnf and horn propositional formulae"
    - "propositional independence formula variable independence and forgetting"
    - "a knowledge compilation map"

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)