human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "Computational complexity reduction for BN2O networks using similarity of states"
  Abstract: Although probabilistic inference in a general Bayesian belief network is an NP-hard problem, inference computation time can be reduced in most practical cases by exploiting domain knowledge and by making appropriate approximations in the knowledge representation. In this paper we introduce the property of similarity of states and a new method for approximate knowledge representation which is based on this property. We define two or more states of a node to be similar when the likelihood ratio of their probabilities does not depend on the instantiations of the other nodes in the network. We show that the similarity of states exposes redundancies in the joint probability distribution which can be exploited to reduce the computational complexity of probabilistic inference in networks with multiple similar states. For example, we show that a BN2O network|a two layer networks often used in diagnostic problems|can be reduced to a very close network with multiple similar states. Probabilistic inference in the new network can be done in only polynomial time with respect to the size of the network, and the results for queries of practical importance are very close to the results that can be obtained in exponential time with the original network. The error introduced by our reduction converges to zero faster than exponentially with respect to the degree of the polynomial describing the resulting computational complexity.

Connected through hyperedge HE-1 (induced by source paper "Sensitivities: An Alternative to Conditional Probabilities for Bayesian Belief Networks"):
  with members:
    - "Efficient Inference in Bayes Networks As A Combinatorial Optimization Problem"

Connected through hyperedge HE-2 (induced by source paper "State-Space Abstraction for Anytime Evaluation of Probabilistic Networks"):
  with members:
    - "Using Qualitative Relationships for Bounding Probability Distributions"
    - "Incremental Tradeoff Resolution in Qualitative Probabilistic Networks"
    - "Dynamic Belief Networks for Discrete Monitoring"
    - "Introduction to the Special Section on Knowledge-Based Construction of Probabilistic and Decision Models (IEEE Transactions"

Question: Please tell me which class the center node belongs to. The 7 classes are: Neural_Networks, Reinforcement_Learning, Probabilistic_Methods, Theory, Genetic_Algorithms, Case_Based, Rule_Learning. Directly output the class name.

gpt: Probabilistic_Methods