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: "A General Lower Bound on the Number of Examples Needed for Learning"
  Abstract: We prove a lower bound of ( 1 * ln 1 ffi + VCdim(C) * ) on the number of random examples required for distribution-free learning of a concept class C, where VCdim(C) is the Vapnik-Chervonenkis dimension and * and ffi are the accuracy and confidence parameters. This improves the previous best lower bound of ( 1 * ln 1 ffi + VCdim(C)), and comes close to the known general upper bound of O( 1 ffi + VCdim(C) * ln 1 * ) for consistent algorithms. We show that for many interesting concept classes, including kCNF and kDNF, our bound is actually tight to within a constant factor.

Connected through hyperedge HE-1 (induced by source paper "Inductive Logic Programming"):
  with members:
    - "LEARNING CONCEPTS BY ASKING QUESTIONS"

Connected through hyperedge HE-2 (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"
    - "Pac Learning, Noise, and Geometry"
    - "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-3 (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"
    - "PAC-Learning PROLOG clauses with or without errors"
    - "Learning from Incomplete Boundary Queries Using Split Graphs and Hypergraphs (Extended Abstract)"

Connected through hyperedge HE-4 (induced by source paper "Metric Entropy and Minimax Risk in Classification"):
  with members:
    - "Consistency of Posterior Distributions for Neural Networks"

Connected through hyperedge HE-5 (induced by source paper "Sequential PAC Learning"):
  with members:
    - "Apple Tasting and Nearly One-Sided Learning"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"

Connected through hyperedge HE-6 (induced by source paper "Prediction, Learning, Uniform Convergence, and Scale-sensitive Dimensions"):
  with members:
    - "Toward Efficient Agnostic Learning"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"

Connected through hyperedge HE-7 (induced by source paper "PAC Learning of One-Dimensional Patterns"):
  with members:
    (none)

Connected through hyperedge HE-8 (induced by source paper "Rigorous Learning Curve Bounds from Statistical Mechanics"):
  with members:
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"
    - "Self bounding learning algorithms"
    - "Markov Decision Processes in Large State Spaces"
    - "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards"
    - "Where Genetic Algorithms Excel"
    - "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"
    - "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"
    - "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with"

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