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: "searching for bayesian network structures in the space of restricted acyclic partially directed graphs"
  Abstract: Although many algorithms have been designed to construct Bayesian network structures using different approaches and principles, they all employ only two methods: those based on independence criteria, and those based on a scoring function and a search procedure (although some methods combine the two). Within the score+search paradigm, the dominant approach uses local search methods in the space of directed acyclic graphs (DAGs), where the usual choices for defining the elementary modifications (local changes) that can be applied are arc addition, arc deletion, and arc reversal. In this paper, we propose a new local search method that uses a different search space, and which takes account of the concept of equivalence between network structures: restricted acyclic partially directed graphs (RPDAGs). In this way, the number of different configurations of the search space is reduced, thus improving efficiency. Moreover, although the final result must necessarily be a local optimum given the nature of the search method, the topology of the new search space, which avoids making early decisions about the directions of the arcs, may help to find better local optima than those obtained by searching in the DAG space. Detailed results of the evaluation of the proposed search method on several test problems, including the well-known Alarm Monitoring System, are also presented.

Connected through hyperedge HE-1 (induced by source paper "separators and adjustment sets in causal graphs complete criteria and an algorithmic framework"):
  with members:
    - "an algorithm for finding minimum d separating sets in belief networks"
    - "identification of conditional interventional distributions"
    - "adjustment criteria in causal diagrams an algorithmic perspective"
    - "pearl s calculus of intervention is complete"

Connected through hyperedge HE-2 (induced by source paper "approximate causal abstraction"):
  with members:
    - "abstracting causal models"
    - "context specific independence in bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "memetic eda based approaches to comprehensive quality aware automated semantic web service composition"):
  with members:
    - "a new similarity measure for taxonomy based on edge counting"

Connected through hyperedge HE-4 (induced by source paper "learning binary bayesian networks in polynomial time and sample complexity"):
  with members:
    - "learning linear structural equation models in polynomial time and sample complexity"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "parameterized complexity results for exact bayesian network structure learning"
    - "sparsityboost a new scoring function for learning bayesian network structure"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "bayesian network learning by compiling to weighted max sat"

Connected through hyperedge HE-5 (induced by source paper "comparative benchmarking of causal discovery techniques"):
  with members:
    - "a review on algorithms for constraint based causal discovery"
    - "scaling up greedy equivalence search for continuous variables"
    - "learning sparse causal models is not np hard"
    - "being bayesian about network structure"
    - "causal inference in the presence of latent variables and selection bias"
    - "counterfactual reasoning and learning systems"
    - "adjacency faithfulness and conservative causal inference"

Connected through hyperedge HE-6 (induced by source paper "evaluating causal models by comparing interventional distributions"):
  with members:
    - "testing identifiability of causal effects"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "bayesian structure learning using dynamic programming and mcmc"
    - "adjacency faithfulness and conservative causal inference"
    - "finding optimal bayesian networks"

Connected through hyperedge HE-7 (induced by source paper "inferring coupling of distributed dynamical systems via transfer entropy"):
  with members:
    - "learning the structure of dynamic probabilistic networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a bayesian approach to learning bayesian networks with local structure"
    - "properties of bayesian belief network learning algorithms"

Connected through hyperedge HE-8 (induced by source paper "learning discriminative bayesian networks from high dimensional continuous neuroimaging data"):
  with members:
    - "a construction of bayesian networks from databases based on an mdl principle"
    - "learning gaussian networks"
    - "maximum margin bayesian networks"
    - "exact maximum margin structure learning of bayesian networks"

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)