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: "bayesian network learning by compiling to weighted max sat"
  Abstract: The problem of learning discrete Bayesian networks from data is encoded as a weighted MAX-SAT problem and the MaxWalkSat local search algorithm is used to address it. For each dataset, the per-variable summands of the (BDeu) marginal likelihood for different choices of parents ('family scores') are computed prior to applying MaxWalkSat. Each permissible choice of parents for each variable is encoded as a distinct propositional atom and the associated family score encoded as a 'soft' weighted single-literal clause. Two approaches to enforcing acyclicity are considered: either by encoding the ancestor relation or by attaching a total order to each graph and encoding that. The latter approach gives better results. Learning experiments have been conducted on 21 synthetic datasets sampled from 7 BNs. The largest dataset has 10,000 datapoints and 60 variables producing (for the 'ancestor' encoding) a weighted CNF input file with 19,932 atoms and 269,367 clauses. For most datasets, MaxWalkSat quickly finds BNs with higher BDeu score than the 'true' BN. The effect of adding prior information is assessed. It is further shown that Bayesian model averaging can be effected by collecting BNs generated during the search.

Connected through hyperedge HE-1 (induced by source paper "learning bayesian networks with low rank conditional probability tables"):
  with members:
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "a simple approach for finding the globally optimal bayesian network structure"

Connected through hyperedge HE-2 (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"
    - "searching for bayesian network structures in the space of restricted acyclic partially directed graphs"

Connected through hyperedge HE-3 (induced by source paper "learning chordal markov networks by constraint satisfaction"):
  with members:
    - "optimal junction trees"
    - "advances in bayesian network learning using integer programming"
    - "bayesian network learning with cutting planes"
    - "optimization in smt with la q cost functions"

Connected through hyperedge HE-4 (induced by source paper "advances in bayesian network learning using integer programming"):
  with members:
    - "bayesian network learning with cutting planes"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "an improved admissible heuristic for learning optimal bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "bayesian network learning with cutting planes"):
  with members:
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "on sensitivity of the map bayesian network structure to the equivalent sample size parameter"

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)