human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Learning to model sequences generated by switching distributions"):
  Abstract: We study efficient algorithms for solving the following problem, which we call the switching distributions learning problem. A sequence S = 1 2 : : : n , over a finite alphabet S is generated in the following way. The sequence is a concatenation of K runs, each of which is a consecutive subsequence. Each run is generated by independent random draws from a distribution ~p i over S, where ~p i is an element in a set of distributions f~p 1 ; : : : ; ~p N g. The learning algorithm is given this sequence and its goal is to find approximations of the distributions ~p 1 ; : : : ; ~p N , and give an approximate segmentation of the sequence into its constituting runs. We give an efficient algorithm for solving this problem and show conditions under which the algorithm is guaranteed to work with high probability.

Includes members:
  Member "Learning polynomials with queries: The highly noisy case  task for the case when F":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Learning in the Presence of Malicious Errors")
  Member "Learning With Unreliable Boundary Queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "PAC Learning Intersections of Halfspaces with Membership Queries (Extended Abstract)")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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: Theory