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 Learning Visual Concepts and DNF Formulae"
  Abstract: We consider the problem of learning DNF formulae in the mistake-bound and the PAC models. We develop a new approach, which is called polynomial explainability, that is shown to be useful for learning some new subclasses of DNF (and CNF) formulae that were not known to be learnable before. Unlike previous learnability results for DNF (and CNF) formulae, these subclasses are not limited in the number of terms or in the number of variables per term; yet, they contain the subclasses of k-DNF and k-term-DNF (and the corresponding classes of CNF) as special cases. We apply our DNF results to the problem of learning visual concepts and obtain learning algorithms for several natural subclasses of visual concepts that appear to have no natural boolean counterpart. On the other hand, we show that learning some other natural subclasses of visual concepts is as hard as learning the class of all DNF formulae. We also consider the robustness of these results under various types of noise.

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"
    - "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"
    - "Toward Efficient Agnostic Learning"

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

Connected through hyperedge HE-3 (induced by source paper "Learning Conjunctions of Horn Clauses"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Teaching a Smarter Learner"
    - "Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis"
    - "Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle"
    - "Learning Read-Once Formulas with Queries"
    - "Learning Boolean Read-Once Formulas over Generalized Bases"
    - "Asking Questions to Minimize Errors"
    - "On Learning Read-k-Satisfy-j DNF"

Connected through hyperedge HE-4 (induced by source paper "Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis"):
  with members:
    - "Toward Efficient Agnostic Learning"
    - "A Quantum Computational Learning Algorithm"
    - "Learning Conjunctions of Horn Clauses"
    - "On Learning Read-k-Satisfy-j DNF"
    - "Implementation Issues in the Fourier Transform Algorithm"
    - "An O(n log log n Learning Algorithm for DNF under the Uniform Distribution"
    - "Learning Using Group Representations (Extended Abstract)"

Connected through hyperedge HE-5 (induced by source paper "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"):
  with members:
    - "25 Learning in Hybrid Noise Environments Using Statistical Queries"
    - "Learning Sparse Perceptrons"
    - "On the Sample Complexity of Noise-Tolerant Learning"
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"

Connected through hyperedge HE-6 (induced by source paper "On Learning Read-k-Satisfy-j DNF"):
  with members:
    - "Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis"
    - "Learning Conjunctions of Horn Clauses"
    - "Learning Read-Once Formulas with 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