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: "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.

Connected through hyperedge HE-1 (induced by source paper "Learning With Unreliable Boundary Queries"):
  with members:
    - "Learning from Incomplete Boundary Queries Using Split Graphs and Hypergraphs (Extended Abstract)"
    - "PAC Learning Intersections of Halfspaces with Membership Queries (Extended Abstract)"

Connected through hyperedge HE-2 (induced by source paper "Learning polynomials with queries: The highly noisy case  task for the case when F"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "Toward Efficient Agnostic Learning"
    - "Exact Identification of Read-once Formulas Using Fixed Points of Amplification Functions"

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: Theory