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: "Exact Identification of Read-once Formulas Using Fixed Points of Amplification Functions"
  Abstract: In this paper we describe a new technique for exactly identifying certain classes of read-once Boolean formulas. The method is based on sampling the input-output behavior of the target formula on a probability distribution that is determined by the fixed point of the formula's amplification function (defined as the probability that a 1 is output by the formula when each input bit is 1 independently with probability p). By performing various statistical tests on easily sampled variants of the fixed-point distribution, we are able to efficiently infer all structural information about any logarithmic-depth formula (with high probability). We apply our results to prove the existence of short universal identification sequences for large classes of formulas. We also describe extensions of our algorithms to handle high rates of noise, and to learn formulas of unbounded depth in Valiant's model with respect to specific distributions. Most of this research was carried out while all three authors were at MIT Laboratory for Computer Science with support provided by ARO Grant DAAL03-86-K-0171, DARPA Contract N00014-89-J-1988, NSF Grant CCR-88914428, and a grant from the Siemens Corporation. R. Schapire received additional support from AFOSR Grant 89-0506 while at Harvard University. S. Goldman is currently supported in part by a G.E. Foundation Junior Faculty Grant and NSF Grant CCR-9110108.

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 "Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "An Interactive Model of Teaching"
    - "Learning Conjunctions of Horn Clauses"
    - "Learning Read-Once Formulas with Queries"
    - "Learning k-term DNF Formulas with an Incomplete Membership Oracle"

Connected through hyperedge HE-3 (induced by source paper "On the Sample Complexity of Weakly Learning"):
  with members:
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Teaching a Smarter Learner"

Connected through hyperedge HE-4 (induced by source paper "Learning k-term DNF Formulas with an Incomplete Membership Oracle"):
  with members:
    - "Learning from Incomplete Boundary Queries Using Split Graphs and Hypergraphs (Extended Abstract)"
    - "Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle"
    - "Learning Conjunctions of Horn Clauses"
    - "Learning Read-Once Formulas with Queries"
    - "Warning: missing six few referencesfixed in proceedings. Learning with Queries but Incomplete Information (Extended Abstract)"

Connected through hyperedge HE-5 (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"
    - "On the Sample Complexity of Weakly Learning"

Connected through hyperedge HE-6 (induced by source paper "Learning polynomials with queries: The highly noisy case  task for the case when F"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "Toward Efficient Agnostic Learning"
    - "Learning to model sequences generated by switching distributions"

Connected through hyperedge HE-7 (induced by source paper "Learning Boolean Read-Once Formulas over Generalized Bases"):
  with members:
    - "Learning Conjunctions of Horn Clauses"
    - "Learning Read-Once Formulas with Queries"
    - "Asking Questions to Minimize Errors"

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