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: "Efficient Distribution-free Learning of Probabilistic Concepts"
  Abstract: In this paper we investigate a new formal model of machine learning in which the concept (boolean function) to be learned may exhibit uncertain or probabilistic behavior|thus, the same input may sometimes be classified as a positive example and sometimes as a negative example. Such probabilistic concepts (or p-concepts) may arise in situations such as weather prediction, where the measured variables and their accuracy are insufficient to determine the outcome with certainty. We adopt from the Valiant model of learning [27] the demands that learning algorithms be efficient and general in the sense that they perform well for a wide class of p-concepts and for any distribution over the domain. In addition to giving many efficient algorithms for learning natural classes of p-concepts, we study and develop in detail an underlying theory of learning p-concepts.

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"
    - "PAC-Learning PROLOG clauses with or without errors"
    - "On Learning Conjunctions with Malicious Noise"

Connected through hyperedge HE-2 (induced by source paper "Prediction, Learning, Uniform Convergence, and Scale-sensitive Dimensions"):
  with members:
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Toward Efficient Agnostic Learning"

Connected through hyperedge HE-3 (induced by source paper "Boosting a weak learning algorithm by majority To be published in Information and Computation"):
  with members:
    - "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"
    - "Sifting informative examples from a random source."
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Learning From a Population of Hypotheses"
    - "Game Theory, On-line Prediction and Boosting"
    - "On the Sample Complexity of Weakly Learning"
    - "A Quantum Computational Learning Algorithm"

Connected through hyperedge HE-4 (induced by source paper "PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-instance Examples"):
  with members:
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Approximating Hyper-Rectangles: Learning and Pseudo-random Sets"
    - "Solving the Multiple-Instance Problem with Axis-Parallel Rectangles"
    - "A Note on Learning from Multiple-Instance Examples"
    - "A Framework for Multiple-Instance Learning"

Connected through hyperedge HE-5 (induced by source paper "Learning Switching Concepts"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "Toward Efficient Agnostic Learning"

Connected through hyperedge HE-6 (induced by source paper "Toward Efficient Agnostic Learning"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Learning in the Presence of Malicious Errors"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Prediction, Learning, Uniform Convergence, and Scale-sensitive Dimensions"
    - "Tracking Drifting Concepts By Minimizing Disagreements"
    - "On Learning Conjunctions with Malicious Noise"
    - "Learning Switching Concepts"
    - "On the Complexity of Function Learning"

Connected through hyperedge HE-7 (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"
    - "PAC-Learning PROLOG clauses with or without errors"
    - "Toward Efficient Agnostic Learning"
    - "On the Sample Complexity of Weakly Learning"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"

Connected through hyperedge HE-8 (induced by source paper "How to Use Expert Advice (Extended Abstract)"):
  with members:
    - "Online Learning versus O*ine Learning"
    - "Simulating Access to Hidden Information while Learning"
    - "Toward Efficient Agnostic Learning"
    - "Context-sensitive learning methods for text categorization"
    - "WORST CASE PREDICTION OVER SEQUENCES UNDER LOG LOSS"
    - "Large Margin Classification Using the Perceptron Algorithm"
    - "An Efficient Extension to Mixture Techniques for Prediction and Decision Trees"
    - "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs"

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