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: "PAC-Learning PROLOG clauses with or without errors"
  Abstract: In a nutshell we can describe a generic ILP problem as following: given a set E of (positive and negative) examples of a target predicate, and some background knowledge B about the world (usually a logic program including facts and auxiliary predicates), the task is to find a logic program H (our hypothesis) such that all positive examples can be deduced from B and H, while no negative example can. In this paper we review some of the results achieved in this area and discuss the techniques used. Moreover we prove the following new results: * Predicates described by non-recursive, local clauses of at most k literals are PAC-learnable under any distribution. This generalizes a previous result that was valid only for constrained clauses. * Predicates that are described by k non-recursive local clauses are PAC-learnable under any distribution. This generalizes a previous result that was non construc tive and valid only under some class of distributions. Finally we introduce what we believe is the first theoretical framework for learning Prolog clauses in the presence of errors. To this purpose we introduce a new noise model, that we call the fixed attribute noise model, for learning propositional concepts over the Boolean domain. This new noise model can be of its own interest.

Connected through hyperedge HE-1 (induced by source paper "Learning in the Presence of Malicious Errors"):
  with members:
    - "25 Learning in Hybrid Noise Environments Using Statistical Queries"
    - "Statistical Queries and Faulty PAC Oracles"
    - "On Learning Visual Concepts and DNF Formulae"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Pac Learning, Noise, and Geometry"
    - "Tracking Drifting Concepts By Minimizing Disagreements"
    - "On Learning Conjunctions with Malicious Noise"
    - "Toward Efficient Agnostic Learning"

Connected through hyperedge HE-2 (induced by source paper "Pac Learning, Noise, and Geometry"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "On Learning from Noisy and Incomplete Examples"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Learning from Incomplete Boundary Queries Using Split Graphs and Hypergraphs (Extended Abstract)"

Connected through hyperedge HE-3 (induced by source paper "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"):
  with members:
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "Learning DFA from Simple Examples"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Sequential PAC Learning"
    - "Toward Efficient Agnostic Learning"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "On the Sample Complexity of Weakly Learning"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"

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