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: "Belief Propagation and Revision in Networks with Loops"
  Abstract: Local belief propagation rules of the sort proposed by Pearl (1988) are guaranteed to converge to the optimal beliefs for singly connected networks. Recently, a number of researchers have empirically demonstrated good performance of these same algorithms on networks with loops, but a theoretical understanding of this performance has yet to be achieved. Here we lay a foundation for an understanding of belief propagation in networks with loops. For networks with a single loop, we derive an analytical relationship between the steady state beliefs in the loopy network and the true posterior probability. Using this relationship we show a category of networks for which the MAP estimate obtained by belief update and by belief revision can be proven to be optimal (although the beliefs will be incorrect). We show how nodes can use local information in the messages they receive in order to correct the steady state beliefs. Furthermore we prove that for all networks with a single loop, the MAP estimate obtained by belief revision at convergence is guaranteed to give the globally optimal sequence of states. The result is independent of the length of the cycle and the size of the state space. For networks with multiple loops, we introduce the concept of a "balanced network" and show simulation results comparing belief revision and update in such networks. We show that the Turbo code structure is balanced and present simulations on a toy Turbo code problem indicating the decoding obtained by belief revision at convergence is significantly more likely to be correct. This report describes research done at the Center for Biological and Computational Learning and the Department of Brain and Cognitive Sciences of the Massachusetts Institute of Technology. Support for the Center is provided in part by a grant from the National Science Foundation under contract ASC-9217041. YW was also supported by NEI R01 EY11005 to E. H. Adelson

Connected through hyperedge HE-1 (induced by source paper "Probabilistic Independence Networks for Hidden Markov Probability Models"):
  with members:
    - "On Structured Variational Approximations"
    - "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"
    - "Coupled hidden Markov models for complex action recognition"
    - "Tractable Inference for Complex Stochastic Processes"
    - "Space-efficient inference in dynamic probabilistic networks"
    - "Speech Recognition with Dynamic Bayesian Networks"

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