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, Noise, and Geometry"
  Abstract: This paper describes the probably approximately correct model of concept learning, paying special attention to the case where instances are points in Euclidean n-space. The problem of learning from noisy training data is also studied.

Connected through hyperedge HE-1 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "On the Sample Complexity of Noise-Tolerant Learning"
    - "Rigorous Learning Curve Bounds from Statistical Mechanics"
    - "Sequential PAC Learning"
    - "Sifting informative examples from a random source."
    - "Approximating Hyper-Rectangles: Learning and Pseudo-random Sets"
    - "Inductive Logic Programming"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"

Connected through hyperedge HE-2 (induced by source paper "On Learning from Noisy and Incomplete Examples"):
  with members:
    - "25 Learning in Hybrid Noise Environments Using Statistical Queries"
    - "Statistical Queries and Faulty PAC Oracles"
    - "Improved Noise-Tolerant Learning and Generalized Statistical Queries"

Connected through hyperedge HE-3 (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"
    - "Tracking Drifting Concepts By Minimizing Disagreements"
    - "PAC-Learning PROLOG clauses with or without errors"
    - "On Learning Conjunctions with Malicious Noise"
    - "Toward Efficient Agnostic Learning"

Connected through hyperedge HE-4 (induced by source paper "PAC-Learning PROLOG clauses with or without errors"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"

Connected through hyperedge HE-5 (induced by source paper "Learning from Incomplete Boundary Queries Using Split Graphs and Hypergraphs (Extended Abstract)"):
  with members:
    - "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)"
    - "Learning With Unreliable Boundary Queries"

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