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 new algorithm for finding map assignments to belief networks"
  Abstract: We present a new algorithm for finding maximum a-posterior) (MAP) assignments of values to belief networks. The belief network is compiled into a network consisting only of nodes with boolean (i.e. only 0 or 1) conditional probabilities. The MAP assignment is then found using a best-first search on the resulting network. We argue that, as one would anticipate, the algorithm is exponential for the general case, but only linear in the size of the network for poly trees.

Connected through hyperedge HE-1 (induced by source paper "path finding under uncertainty through probabilistic inference"):
  with members:
    - "selecting computations theory and applications"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "church a language for generative models"
    - "complexity of canadian traveler problem variants"
    - "the fast downward planning system"

Connected through hyperedge HE-2 (induced by source paper "an efficient approach for finding the mpe in belief networks"):
  with members:
    - "qualitative propagation and scenario based explanation of probabilistic reasoning"
    - "on the generation of alternative explanations with implications for belief revision"
    - "d separation from theorems to algorithms"

Connected through hyperedge HE-3 (induced by source paper "mini bucket heuristics for improved search"):
  with members:
    - "a scheme for approximating probabilistic inference"
    - "empirical evaluation of approximation algorithms for probabilistic decoding"

Connected through hyperedge HE-4 (induced by source paper "the use of conflicts in searching bayesian networks"):
  with members:
    - "pruning bayesian networks for efficient computation"
    - "search based methods to bound diagnostic probabilities in very large belief nets"

Connected through hyperedge HE-5 (induced by source paper "representing bayesian networks within probabilistic horn abduction"):
  with members:
    - "towards solving the multiple extension problem combining defaults and probabilities"
    - "what is an optimal diagnosis"

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)