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: "Learning in the Presence of Malicious Errors"
  Abstract: In this paper we study an extension of the distribution-free model of learning introduced by Valiant [23] (also known as the probably approximately correct or PAC model) that allows the presence of malicious errors in the examples given to a learning algorithm. Such errors are generated by an adversary with unbounded computational power and access to the entire history of the learning algorithm's computation. Thus, we study a worst-case model of errors. Our results include general methods for bounding the rate of error tolerable by any learning algorithm, efficient algorithms tolerating nontrivial rates of malicious errors, and equivalences between problems of learning with errors and standard combinatorial optimization problems.

Connected through hyperedge HE-1 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning"):
  with members:
    - "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"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"

Connected through hyperedge HE-2 (induced by source paper "Efficient Distribution-free Learning of Probabilistic Concepts"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "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-3 (induced by source paper "Statistical Queries and Faulty PAC Oracles"):
  with members:
    - "25 Learning in Hybrid Noise Environments Using Statistical Queries"
    - "On Learning from Noisy and Incomplete Examples"
    - "On Learning Visual Concepts and DNF Formulae"

Connected through hyperedge HE-4 (induced by source paper "Pac Learning, Noise, and Geometry"):
  with members:
    - "On Learning from Noisy and Incomplete Examples"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "PAC-Learning PROLOG clauses with or without errors"
    - "Learning from Incomplete Boundary Queries Using Split Graphs and Hypergraphs (Extended Abstract)"

Connected through hyperedge HE-5 (induced by source paper "25 Learning in Hybrid Noise Environments Using Statistical Queries"):
  with members:
    - "Statistical Queries and Faulty PAC Oracles"
    - "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"
    - "Improved Noise-Tolerant Learning and Generalized Statistical Queries"
    - "On Learning from Noisy and Incomplete Examples"

Connected through hyperedge HE-6 (induced by source paper "Exact Identification of Read-once Formulas Using Fixed Points of Amplification Functions"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "On the Sample Complexity of Weakly Learning"
    - "Learning polynomials with queries: The highly noisy case  task for the case when F"
    - "Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle"
    - "Learning Boolean Read-Once Formulas over Generalized Bases"
    - "Learning k-term DNF Formulas with an Incomplete Membership Oracle"

Connected through hyperedge HE-7 (induced by source paper "PAC-Learning PROLOG clauses with or without errors"):
  with members:
    - "Pac Learning, Noise, and Geometry"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"

Connected through hyperedge HE-8 (induced by source paper "On Learning Visual Concepts and DNF Formulae"):
  with members:
    - "Statistical Queries and Faulty PAC Oracles"
    - "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"
    - "Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis"
    - "Learning Conjunctions of Horn Clauses"
    - "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