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: "Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis"
  Abstract: We present new results, both positive and negative, on the well-studied problem of learning disjunctive normal form (DNF) expressions. We first prove that an algorithm due to Kushilevitz and Mansour [16] can be used to weakly learn DNF using membership queries in polynomial time, with respect to the uniform distribution on the inputs. This is the first positive result for learning unrestricted DNF expressions in polynomial time in any nontrivial formal model of learning. It provides a sharp contrast with the results of Kharitonov [15], who proved that AC 0 is not efficiently learnable in the same model (given certain plausible cryptographic assumptions). We also present efficient learning algorithms in various models for the read-k and SAT-k subclasses of DNF. For our negative results, we turn our attention to the recently introduced statistical query model of learning [11]. This model is a restricted version of the popular Probably Approximately Correct (PAC) model [23], and practically every class known to be efficiently learnable in the PAC model is in fact learnable in the statistical query model [11]. Here we give a general characterization of the complexity of statistical query learning in terms of the number of uncorrelated functions in the concept class. This is a distribution-dependent quantity yielding upper and lower bounds on the number of statistical queries required for learning on any input distribution. As a corollary, we obtain that DNF expressions and decision trees are not even weakly learnable with fl This research is sponsored in part by the Wright Laboratory, Aeronautical Systems Center, Air Force Materiel Command, USAF, and the Advanced Research Projects Agency (ARPA) under grant number F33615-93-1-1330. Support also is sponsored by the National Science Foundation under Grant No. CC-9119319. Blum also supported in part by NSF National Young Investigator grant CCR-9357793. Views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing official policies or endorsements, either expressed or implied, of Wright Laboratory or the United States Government, or NSF. respect to the uniform input distribution in polynomial time in the statistical query model. This result is information-theoretic and therefore does not rely on any unproven assumptions. It demonstrates that no simple modification of the existing algorithms in the computational learning theory literature for learning various restricted forms of DNF and decision trees from passive random examples (and also several algorithms proposed in the experimental machine learning communities, such as the ID3 algorithm for decision trees [22] and its variants) will solve the general problem. The unifying tool for all of our results is the Fourier analysis of a finite class of boolean functions on the hypercube.

Connected through hyperedge HE-1 (induced by source paper "Learning Conjunctions of Horn Clauses"):
  with members:
    - "On Learning Visual Concepts and DNF Formulae"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Teaching a Smarter Learner"
    - "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-2 (induced by source paper "A Quantum Computational Learning Algorithm"):
  with members:
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"

Connected through hyperedge HE-3 (induced by source paper "On Learning Visual Concepts and DNF Formulae"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "Statistical Queries and Faulty PAC Oracles"
    - "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"
    - "Learning Conjunctions of Horn Clauses"
    - "On Learning Read-k-Satisfy-j DNF"

Connected through hyperedge HE-4 (induced by source paper "Implementation Issues in the Fourier Transform Algorithm"):
  with members:
    - "An O(n log log n Learning Algorithm for DNF under the Uniform Distribution"

Connected through hyperedge HE-5 (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"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"

Connected through hyperedge HE-6 (induced by source paper "An O(n log log n Learning Algorithm for DNF under the Uniform Distribution"):
  with members:
    - "Implementation Issues in the Fourier Transform Algorithm"
    - "Learning Using Group Representations (Extended Abstract)"

Connected through hyperedge HE-7 (induced by source paper "On Learning Read-k-Satisfy-j DNF"):
  with members:
    - "On Learning Visual Concepts and DNF Formulae"
    - "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