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: "d separation from theorems to algorithms"
  Abstract: An efficient algorithm is developed that identifies all independencies implied by the topology of a Bayesian network. Its correctness and maximality stems from the soundness and completeness of d-separation with respect to probability theory. The algorithm runs in time O (l E l) where E is the number of edges in the network.

Connected through hyperedge HE-1 (induced by source paper "query the model precomputations for efficient inference with bayesian networks"):
  with members:
    - "a simple cache model for image recognition"
    - "generative neural machine translation"
    - "database learning toward a database that becomes smarter every time"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-2 (induced by source paper "learning bayesian networks from demographic and health survey data"):
  with members:
    - "evaluating structure learning algorithms with a balanced scoring function"
    - "exposing the probabilistic causal structure of discrimination"
    - "generating connected acyclic digraphs uniformly at random"

Connected through hyperedge HE-3 (induced by source paper "a rational distributed process level account of independence judgment"):
  with members:
    - "the causal frame problem an algorithmic perspective"

Connected through hyperedge HE-4 (induced by source paper "exploiting causality for selective belief filtering in dynamic bayesian networks"):
  with members:
    - "vector space analysis of belief state approximation for pomdps"
    - "causal independence for knowledge acquisition and inference"
    - "tractable inference for complex stochastic processes"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "context specific independence in bayesian networks"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "value directed belief state approximation for pomdps"

Connected through hyperedge HE-5 (induced by source paper "constructing situation specific belief networks"):
  with members:
    - "pruning bayesian networks for efficient computation"
    - "a theoretical framework for context sensitive temporal probability model construction with application to plan projection"
    - "generating bayesian networks from probability logic knowledge bases"
    - "network fragments representing knowledge for constructing probabilistic models"
    - "integrating model construction and evaluation"
    - "tradeoffs in constructing and evaluating temporal influence diagrams"
    - "computational advantages of relevance reasoning in bayesian belief networks"

Connected through hyperedge HE-6 (induced by source paper "lazy evaluation of symmetric bayesian decision problems"):
  with members:
    - "decision making using probabilistic inference methods"
    - "from influence diagrams to junction trees"
    - "lazy propagation in junction trees"
    - "a method for using belief networks as influence diagrams"

Connected through hyperedge HE-7 (induced by source paper "using potential influence diagrams for probabilistic inference and decision making"):
  with members:
    - "decision making using probabilistic inference methods"
    - "a fusion algorithm for solving bayesian decision problems"
    - "directed reduction algorithms and decomposable graphs"
    - "evidence absorption and propagation through evidence reversals"

Connected through hyperedge HE-8 (induced by source paper "an efficient approach for finding the mpe in belief networks"):
  with members:
    - "a new algorithm for finding map assignments to belief networks"
    - "qualitative propagation and scenario based explanation of probabilistic reasoning"
    - "on the generation of alternative explanations with implications for belief revision"

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)