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 logical characterization of constraint based causal discovery"
  Abstract: We present a novel approach to constraint-based causal discovery, that takes the form of straightforward logical inference, applied to a list of simple, logical statements about causal relations that are derived directly from observed (in)dependencies. It is both sound and complete, in the sense that all invariant features of the corresponding partial ancestral graph (PAG) are identified, even in the presence of latent variables and selection bias. The approach shows that every identifiable causal relation corresponds to one of just two fundamental forms. More importantly, as the basic building blocks of the method do not rely on the detailed (graphical) structure of the corresponding PAG, it opens up a range of new opportunities, including more robust inference, detailed accountability, and application to large models.

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 "ancestral causal inference"):
  with members:
    - "clingo asp control preliminary report"
    - "causal discovery from changes"
    - "cyclic causal discovery from continuous equilibrium data"
    - "incorporating causal prior knowledge as path constraints in bayesian networks and maximal ancestral graphs"
    - "adjacency faithfulness and conservative causal inference"
    - "a bayesian approach to constraint based causal inference"

Connected through hyperedge HE-3 (induced by source paper "from dependency to causality a machine learning approach"):
  with members:
    - "inferring deterministic causal relations"

Connected through hyperedge HE-4 (induced by source paper "reasoning about independence in probabilistic models of relational data"):
  with members:
    - "a factorization criterion for acyclic directed mixed graphs"
    - "on the logic of causal models"
    - "causal networks semantics and expressiveness"
    - "directed cyclic graphical representations of feedback models"
    - "a sound and complete algorithm for learning causal models from relational data"
    - "causal inference in the presence of latent variables and selection bias"
    - "discriminative probabilistic models for relational data"
    - "operations for learning with graphical models"

Connected through hyperedge HE-5 (induced by source paper "learning sparse causal models is not np hard"):
  with members:
    - "bayesian network learning with cutting planes"
    - "an improved admissible heuristic for learning optimal bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "a bayesian approach to constraint based causal inference"):
  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"
    - "adjacency faithfulness and conservative causal inference"

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)