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 With Unreliable Boundary Queries"
  Abstract: We introduce a model for learning from examples and membership queries in situations where the boundary between positive and negative examples is somewhat ill-defined. In our model, queries near the boundary of a target concept may receive incorrect or "don't care" responses, and the distribution of examples has zero probability mass on the boundary region. The motivation behind our model is that in many cases the boundary between positive and negative examples is complicated or "fuzzy." However, one may still hope to learn successfully, because the typical examples that one sees do not come from that region. We present several positive results in this new model. We show how to learn the intersection of two arbitrary halfspaces when membership queries near the boundary may be answered incorrectly. Our algorithm is an extension of an algorithm of Baum [7, 6] that learns the intersection of two halfspaces whose bounding planes pass through the origin in the PAC-with-membership-queries model. We also describe algorithms for learning several subclasses of monotone DNF formulas.

Connected through hyperedge HE-1 (induced by source paper "PAC Learning Intersections of Halfspaces with Membership Queries (Extended Abstract)"):
  with members:
    - "Toward Efficient Agnostic Learning"
    - "Noise-Tolerant Parallel Learning of Geometric Concepts"

Connected through hyperedge HE-2 (induced by source paper "Learning from Incomplete Boundary Queries Using Split Graphs and Hypergraphs (Extended Abstract)"):
  with members:
    - "Pac Learning, Noise, and Geometry"
    - "Learning k-term DNF Formulas with an Incomplete Membership Oracle"
    - "Warning: missing six few referencesfixed in proceedings. Learning with Queries but Incomplete Information (Extended Abstract)"

Connected through hyperedge HE-3 (induced by source paper "Learning to model sequences generated by switching distributions"):
  with members:
    - "Learning polynomials with queries: The highly noisy case  task for the case when F"

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