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: "Trellis-Constrained Codes"
  Abstract: We introduce a class of iteratively decodable trellis-constrained codes as a generalization of turbocodes, low-density parity-check codes, serially-concatenated convolutional codes, and product codes. In a trellis-constrained code, multiple trellises interact to define the allowed set of codewords. As a result of these interactions, the minimum-complexity single trellis for the code can have a state space that grows exponentially with block length. However, as with turbocodes and low-density parity-check codes, a decoder can approximate bit-wise maximum a posteriori decoding by using the sum-product algorithm on the factor graph that describes the code. We present two new families of codes, homogenous trellis-constrained codes and ring-connected trellis-constrained codes, and give results that show these codes perform in the same regime as do turbo-codes and low-density parity-check codes.

Connected through hyperedge HE-1 (induced by source paper "Factor Graphs and Algorithms"):
  with members:
    (none)

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