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: "Approximating Hyper-Rectangles: Learning and Pseudo-random Sets"
  Abstract: The PAC learning of rectangles has been studied because they have been found experimentally to yield excellent hypotheses for several applied learning problems. Also, pseudorandom sets for rectangles have been actively studied recently because (i) they are a subprob-lem common to the derandomization of depth-2 (DNF) circuits and derandomizing Randomized Logspace, and (ii) they approximate the distribution of n independent multivalued random variables. We present improved upper bounds for a class of such problems of approximating high-dimensional rectangles that arise in PAC learning and pseudorandomness.

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."
    - "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 "Solving the Multiple-Instance Problem with Axis-Parallel Rectangles"):
  with members:
    - "PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-instance Examples"
    - "Generalizing from Case Studies: A Case Study"
    - "A Note on Learning from Multiple-Instance Examples"
    - "A Framework for Multiple-Instance Learning"
    - "Lookahead and Discretization in ILP"
    - "A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction"

Connected through hyperedge HE-3 (induced by source paper "PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-instance Examples"):
  with members:
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "Solving the Multiple-Instance Problem with Axis-Parallel Rectangles"
    - "A Note on Learning from Multiple-Instance Examples"
    - "A Framework for Multiple-Instance Learning"

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