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: "On the Sample Complexity of Weakly Learning"
  Abstract: In this paper, we study the sample complexity of weak learning. That is, we ask how much data must be collected from an unknown distribution in order to extract a small but significant advantage in prediction. We show that it is important to distinguish between those learning algorithms that output deterministic hypotheses and those that output randomized hypotheses. We prove that in the weak learning model, any algorithm using deterministic hypotheses to weakly learn a class of Vapnik-Chervonenkis dimension d(n) requires ( d(n)) examples. In contrast, when randomized hypotheses are allowed, we show that fi(1) examples suffice in some cases. We then show that there exists an efficient algorithm using deterministic hypotheses that weakly learns against any distribution on a set of size d(n) with only O(d(n) 2=3 ) examples. Thus for the class of symmetric Boolean functions over n variables, where the strong learning sample complexity is fi(n), the sample complexity for weak learning using deterministic hypotheses is ( n) and O(n 2=3 ), and the sample complexity for weak learning using randomized hypotheses is fi(1). Next we prove the existence of classes for which the distribution-free sample size required to obtain a slight advantage in prediction over random guessing is essentially equal to that required to obtain arbitrary accuracy. Finally, for a class of small circuits, namely all parity functions of subsets of n Boolean variables, we prove a weak learning sample complexity of fi(n). This bound holds even if the weak learning algorithm is allowed to replace random sampling with membership queries, and the target distribution is uniform on f0; 1g n . p

Connected through hyperedge HE-1 (induced by source paper "Exact Identification of Read-once Formulas Using Fixed Points of Amplification Functions"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Learning polynomials with queries: The highly noisy case  task for the case when F"
    - "Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle"
    - "Learning Boolean Read-Once Formulas over Generalized Bases"
    - "Learning k-term DNF Formulas with an Incomplete Membership Oracle"

Connected through hyperedge HE-2 (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"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "Learning From a Population of Hypotheses"
    - "Game Theory, On-line Prediction and Boosting"
    - "A Quantum Computational Learning Algorithm"

Connected through hyperedge HE-3 (induced by source paper "Teaching a Smarter Learner"):
  with members:
    - "The Power of Self-Directed Learning"
    - "Learning Conjunctions of Horn Clauses"

Connected through hyperedge HE-4 (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"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "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