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: "Some Comments on the Information Stored in Sparse Distributed Memory"
  Abstract: Report R95:11 ISRN : SICS-R--95/11-SE ISSN : 0283-3638 Abstract We consider a sparse distributed memory with randomly chosen hard locations, in which an unknown number T of random data vectors have been stored. A method is given to estimate T from the content of the memory with high accuracy. In fact, our estimate is unbiased, the coefficient of variation being roughly inversely proportional to p MU , where M is the number of hard locations in the memory and U the length of data, so the accuracy can be made arbitrarily high by making the memory big enough. A consequence of this is that the good reading methods in [5] and [6] can be used without any need for the special extra location introduced there.

Connected through hyperedge HE-1 (induced by source paper "Convergence and new operations in SDM new method for converging in the SDM memory, utilizing"):
  with members:
    - "Best Probability of Activation and Performance Comparisons for Several Designs of Sparse Distributed Memory"
    - "Capacity of SDM"

Connected through hyperedge HE-2 (induced by source paper "Capacity of SDM"):
  with members:
    - "Best Probability of Activation and Performance Comparisons for Several Designs of Sparse Distributed Memory"
    - "Convergence and new operations in SDM new method for converging in the SDM memory, utilizing"

Connected through hyperedge HE-3 (induced by source paper "Best Probability of Activation and Performance Comparisons for Several Designs of Sparse Distributed Memory"):
  with members:
    - "Capacity of SDM"
    - "Convergence and new operations in SDM new method for converging in the SDM memory, utilizing"

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: Neural_Networks