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: "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
  Abstract: In this paper we prove the intractability of learning several classes of Boolean functions in the distribution-free model (also called the Probably Approximately Correct or PAC model) of learning from examples. These results are representation independent, in that they hold regardless of the syntactic form in which the learner chooses to represent its hypotheses. Our methods reduce the problems of cracking a number of well-known public-key cryptosys- tems to the learning problems. We prove that a polynomial-time learning algorithm for Boolean formulae, deterministic finite automata or constant-depth threshold circuits would have dramatic consequences for cryptography and number theory: in particular, such an algorithm could be used to break the RSA cryptosystem, factor Blum integers (composite numbers equivalent to 3 modulo 4), and detect quadratic residues. The results hold even if the learning algorithm is only required to obtain a slight advantage in prediction over random guessing. The techniques used demonstrate an interesting duality between learning and cryptography. We also apply our results to obtain strong intractability results for approximating a gener - alization of graph coloring. fl This research was conducted while the author was at Harvard University and supported by an A.T.& T. Bell Laboratories scholarship. y Supported by grants ONR-N00014-85-K-0445, NSF-DCR-8606366 and NSF-CCR-89-02500, DAAL03-86-K-0171, DARPA AFOSR 89-0506, and by SERC.

Connected through hyperedge HE-1 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "On the Sample Complexity of Noise-Tolerant Learning"
    - "Rigorous Learning Curve Bounds from Statistical Mechanics"
    - "Sequential PAC Learning"
    - "Sifting informative examples from a random source."
    - "Approximating Hyper-Rectangles: Learning and Pseudo-random Sets"
    - "Inductive Logic Programming"
    - "Pac Learning, Noise, and Geometry"

Connected through hyperedge HE-2 (induced by source paper "Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks"):
  with members:
    - "Pruning Recurrent Neural Networks for Improved Generalization Performance"
    - "Performance of On-Line Learning Methods in Predicting Multiprocessor Memory Access Patterns"
    - "First-Order vs. Second-Order Single Layer Recurrent Neural Networks"
    - "A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction"

Connected through hyperedge HE-3 (induced by source paper "On the Learnability and Usage of Acyclic Probabilistic Finite Automata"):
  with members:
    - "Learning Probabilistic Automata with Variable Memory Length"
    - "Training Algorithms for Hidden Markov Models Using Entropy Based Distance Functions"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"
    - "On Learning Bounded-Width Branching Programs"

Connected through hyperedge HE-4 (induced by source paper "Efficient Distribution-free Learning of Probabilistic Concepts"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Learning in the Presence of Malicious Errors"
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-instance Examples"
    - "Prediction, Learning, Uniform Convergence, and Scale-sensitive Dimensions"
    - "Toward Efficient Agnostic Learning"
    - "Learning Switching Concepts"

Connected through hyperedge HE-5 (induced by source paper "On Learning Bounded-Width Branching Programs"):
  with members:
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"
    - "On the Learnability and Usage of Acyclic Probabilistic Finite Automata"

Connected through hyperedge HE-6 (induced by source paper "New Evidence Driven State Merging Algorithm"):
  with members:
    - "A Sampling-Based Heuristic for Tree Search Applied to Grammar Induction"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"

Connected through hyperedge HE-7 (induced by source paper "Learning DFA from Simple Examples"):
  with members:
    - "Planning and Learning in an Adversarial Robotic Game"
    - "Query, PACS and simple-PAC Learning"

Connected through hyperedge HE-8 (induced by source paper "Learning Conjunctions of Horn Clauses"):
  with members:
    - "On Learning Visual Concepts and DNF Formulae"
    - "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"

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