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 bayesian approach to learning causal networks"
  Abstract: Whereas acausal Bayesian networks represent probabilistic independence, causal Bayesian networks represent causal relationships. In this paper, we examine Bayesian methods for learning both types of networks. Bayesian methods for learning acausal networks are fairly well developed. These methods often employ assumptions to facilitate the construction of priors, including the assumptions of parameter independence, parameter modularity, and likelihood equivalence. We show that although these assumptions also can be appropriate for learning causal networks, we need additional assumptions in order to learn causal networks. We introduce two sufficient assumptions, called {em mechanism independence} and {em component independence}. We show that these new assumptions, when combined with parameter independence, parameter modularity, and likelihood equivalence, allow us to apply methods for learning acausal networks to learn causal networks.

Connected through hyperedge HE-1 (induced by source paper "a tutorial on learning with bayesian networks"):
  with members:
    - "query dags a practical paradigm for implementing belief network inference"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "asymptotic model selection for directed networks with hidden variables"
    - "directed reduction algorithms and decomposable graphs"
    - "a transformational characterization of equivalent bayesian network structures"
    - "computing upper and lower bounds on likelihoods in intractable networks"
    - "theory refinement on bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "recognizing car fluents from video"):
  with members:
    - "a large scale car dataset for fine grained categorization and verification"
    - "learning to detect vehicles by clustering appearance patterns"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "viewpoints and keypoints"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "detect what you can detecting and representing objects using holistic models and body parts"
    - "a discriminative cnn video representation for event detection"
    - "deformable part models are convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "causal inference in observational data"):
  with members:
    - "causal decision trees"
    - "from observational studies to causal rule mining"
    - "a theoretical study of y structures for causal discovery"

Connected through hyperedge HE-4 (induced by source paper "real time top k predictive query processing over event streams"):
  with members:
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "evidence absorption and propagation through evidence reversals"
    - "ais bn an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-5 (induced by source paper "mining combined causes in large data sets"):
  with members:
    - "from observational studies to causal rule mining"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning module networks"

Connected through hyperedge HE-6 (induced by source paper "learning bayesian networks the combination of knowledge and statistical data"):
  with members:
    - "causality in bayesian belief networks"
    - "a definition and graphical representation for causality"
    - "an algorithm for the construction of bayesian network structures from data"
    - "a transformational characterization of equivalent bayesian network structures"
    - "a construction of bayesian networks from databases based on an mdl principle"
    - "using causal information and local measures to learn bayesian networks"
    - "local expression languages for probabilistic dependence a preliminary report"
    - "deriving a minimal i map of a belief network relative to a target ordering of its nodes"

Connected through hyperedge HE-7 (induced by source paper "a graph theoretic analysis of information value"):
  with members:
    - "reasoning about the value of decision model refinement methods and application"
    - "reformulating inference problems through selective conditioning"
    - "efficient decision theoretic planning techniques and empirical analysis"
    - "ideal reformulation of belief networks"
    - "dynamic construction of belief networks"
    - "chain graphs for learning"
    - "state space abstraction for anytime evaluation of probabilistic networks"
    - "representing context sensitive knowledge in a network formalism a preliminary report"

Connected through hyperedge HE-8 (induced by source paper "asymptotic model selection for directed networks with hidden variables"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "efficient approximations for the marginal likelihood of incomplete data given a bayesian network"
    - "a construction of bayesian networks from databases based on an mdl principle"
    - "using causal information and local measures to learn bayesian networks"
    - "a characterization of the dirichlet distribution with application to learning bayesian networks"
    - "mean field theory for sigmoid belief networks"
    - "operations for learning with graphical models"

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)