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: "Warning: missing six few referencesfixed in proceedings. Learning with Queries but Incomplete Information (Extended Abstract)"
  Abstract: We investigate learning with membership and equivalence queries assuming that the information provided to the learner is incomplete. By incomplete we mean that some of the membership queries may be answered by I don't know. This model is a worst-case version of the incomplete membership query model of Angluin and Slonim. It attempts to model practical learning situations, including an experiment of Lang and Baum that we describe, where the teacher may be unable to answer reliably some queries that are critical for the learning algorithm. We present algorithms to learn monotone k-term DNF with membership queries only, and to learn monotone DNF with membership and equivalence queries. Compared to the complete information case, the query complexity increases by an additive term linear in the number of I don't know answers received. We also observe that the blowup in the number of queries can in general be exponential for both our new model and the incomplete membership model.

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"
    - "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"

Connected through hyperedge HE-2 (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)"
    - "Exact Identification of Read-once Formulas Using Fixed Points of Amplification Functions"
    - "Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle"
    - "Learning Conjunctions of Horn Clauses"
    - "Learning Read-Once Formulas with Queries"

Connected through hyperedge HE-3 (induced by source paper "An Interactive Model of Teaching"):
  with members:
    - "The Power of Self-Directed Learning"
    - "Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle"
    - "Learning Unions of Boxes with Membership and Equivalence Queries"

Connected through hyperedge HE-4 (induced by source paper "Learning from Incomplete Boundary Queries Using Split Graphs and Hypergraphs (Extended Abstract)"):
  with members:
    - "Pac Learning, Noise, and Geometry"
    - "Learning k-term DNF Formulas with an Incomplete Membership Oracle"
    - "Learning With Unreliable Boundary Queries"

Connected through hyperedge HE-5 (induced by source paper "Learning Read-Once Formulas with Queries"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle"
    - "Learning Conjunctions of Horn Clauses"
    - "Learning Boolean Read-Once Formulas over Generalized Bases"
    - "Asking Questions to Minimize Errors"
    - "On Learning Read-k-Satisfy-j DNF"
    - "How Many Queries are Needed to Learn?"
    - "Learning k-term DNF Formulas with an Incomplete Membership Oracle"

Connected through hyperedge HE-6 (induced by source paper "Simulating Access to Hidden Information while Learning"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "On the Complexity of Function Learning"
    - "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